A STRUCTURAL ENGINEERING SUPPORT SYSTEMUSING SEMANTIC COMPUTING

by

Thiti Vacharasintopchai

A dissertation submitted in partial fulfillment of the requirements for thedegree of Doctor of Engineering in Structural Engineering

Examination Commitee: Professor Vilas Wuwongse (Chairperson)Professor Worsak Kanok-Nukulchai (Co-chairperson)Dr. Pennung WarnitchaiDr. B. H. W. Hadikusumo

External Examiner: Professor Kincho H. LawDepartment of Civil and Environmental EngineeringStanford UniversityStanford, CA, U.S.A.

Nationality: Thai

Previous Degrees: B.Eng. (Civil Engineering)Chulalongkorn UniversityBangkok, Thailand

M.Eng. (Structural Engineering)Asian Institute of TechnologyBangkok, Thailand

Scholarship Donor: RTG Fellowship

Asian Institute of TechnologySchool of Engineering and Technology

ThailandDecember 2007

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ACKNOWLEDGMENTS

I would like to thank the Royal Thai Government for granting me adoctoral research fellowship at the Asian Institute of Technology. My pro-found gratitude is due to Professor Vilas Wuwongse and Professor WorsakKanok-Nukulchai, my mentors and advisors who invariably gave highlyvaluable guidance, inspirational suggestions, encouragement and supportthroughout this research, to Professor Kincho H. Law from Stanford Uni-versity who gave highly valuable suggestions as the external examiner, andto Dr. William Barry, former doctoral advisor, master’s thesis advisor andgood friend, who introduced me to the world of scientific computing. Iwould also like to express sincere appreciation for Dr. Pennung Warnitchaiand Dr. B. H. W. Hadikusumo, doctoral committee members, who gaveinvaluable guidance and suggestions. I am grateful to Dr. Pannapa Her-abat for her unfailingly support and to Dr. Wisit U. Pongsa, my formeremployer, who gave several opportunities for conferences and professionaltraining which form the basis of the work in this dissertation. Dr. Vo-ratas Kachitvichyanukul provided useful comments during the early stagesof this research. Wouter Rosingh was of great help particularly towardsthe end. I wish to thank my parents, Chavalert Vacharasintopchai andBenjawan Chatnarat, my younger brothers, Nithi and Nipith, family mem-bers, Apsara Narat and Gallissara Agavatpanitch for their love, supportand encouragement. Thanks are due to friends, especially Dr. DussadeeSatirasetthavee, Dr. Kannapa Pongponrat, Dr. Pongtawat Chippimolchai,Neelawat Intaraksa and Anisara Pensuk for their selfless care and generos-ity during my study at the Institute. Assistance from colleagues at theKnowledge Representation Laboratory and the School of Engineering andTechnology, especially Yupa Soodjitporn and Worranuch Chumchat, areappreciated. I am also grateful to the Asian Development Bank and theGreater Mekong Subregion Academic Network for funding the knowledgemanagement part of this research.

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ABSTRACT

Practicing structural engineers are often faced with limitations in avail-able computing tools as well as insufficient access to relevant knowledge fordesign projects and engineering jobs. At the launch of a project the im-mediate tasks invariably involve research, discussions with colleagues anda series of computations. These tasks are repeated over and over againuntil a final design is arrived at. The degree to which these tasks are wellfacilitated in an engineering firm helps determine the productivity of itsengineers and thus the firm’s competitiveness in the industry. Semanticcomputing technologies such as Semantic Web Services, Weblog, and Dig-ital Library have substantial, hitherto untapped potential to improve theproductivity of structural engineers. This dissertation proposes a supportsystem for structural engineering using these semantic computing technolo-gies. A system architecture and the key software components for it arespecified and prototype systems presented. The support system involvestwo frameworks of software infrastructure: first, the Weblog and DigitalLibrary Framework for Engineering Knowledge Management (Blog+DL),which uses Weblogs and Digital Libraries as core components of a collab-orative structural engineering support system; second, the Semantic WebServices Framework for Computational Mechanics (SWSCM), a method-ology that unifies and utilizes scattered computing resources. Blog+DLand SWSCM are complementary methodologies in the engineering process.Blog+DL relies on SWSCM to integrate computing resources shared by sev-eral parties. SWSCM can use Blog+DL to deploy and discover shared com-puting resources, as well as to educate and exchange knowledge, includingpeer opinions about underlying theories and user experiences. Two proof ofconcept prototype systems were developed. The first system illustrates thejoint application of Blog+DL and SWSCM to build support systems thatfacilitate the computationally oriented knowledge management process instructural engineering. The second system illustrates the full potential ofSWSCM to facilitate a computationally intensive workflow that involvesheterogeneous engineering software components. Blog+DL and SWSCMcombine semantic computing technologies to build computer-aided engi-neering tools that improve the productivity of individual engineers andthereby enhance the competitiveness of engineering firms.

Keywords: engineering support systems, knowledge management, productivity,software interoperability, Semantic Web Services, Semantic Web, Web Services,Internet, ontologies, artificial intelligence

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TABLE OF CONTENTS

Chapter Title Page

Title Page iAcknowledgments iiAbstract iiiTable of Contents ivList of Figures viList of Tables vii

1 Introduction 11.1 Motivation 11.2 Problem Statement 11.3 Objectives 21.4 Scope and Research Approach 21.5 Contributions 31.6 Organization 3

2 Literature Review 42.1 Knowledge Management 4

2.1.1 Definition and Significance 42.1.2 Classification of Knowledge 52.1.3 Theory of Organizational Knowledge Creation 52.1.4 Knowledge Management Process and Tools 6

2.2 Knowledge Management in Engineering Design Firms 62.2.1 DAR Knowledge Management System 72.2.2 ADD and Arup Intranet Systems 7

2.3 Weblogs and Digital Libraries 82.3.1 Weblogs 82.3.2 Digital Libraries 9

2.4 Web Services 102.5 The Semantic Web 12

2.5.1 Background 122.5.2 How the Semantic Web Works 13

2.6 Semantic Web Services 152.6.1 Background 152.6.2 How Semantic Web Services Work 16

3 Development of a Support System for Structural En-

gineering 183.1 Structural Engineers and Construction Projects 183.2 Design Principles 183.3 General System Architecture 203.4 Summary 21

4 A Structural Engineering Support System Using

Semantic Computing 224.1 Introduction 22

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4.2 Blog+DL Framework for Engineering Knowledge Man-agement 224.2.1 Technology Application Framework 224.2.2 System Architecture 24

4.3 A Semantic Computing-Based Support System for Struc-tural Engineers 274.3.1 Implementation 274.3.2 A Proof of Concept Prototype 29

4.4 Summary 31

5 Engineering Software Interoperability 445.1 Introduction 445.2 A Semantic Web Services Framework for Computa-

tional Mechanics 445.2.1 SWSCM Model 455.2.2 Components of SWSCM Service 465.2.3 Communication between SWSCM Services 475.2.4 Inference Engine and Reasoning Process 485.2.5 Matchmaking Algorithm 50

5.3 Application to Structural Analysis and Design 515.3.1 Implementation 515.3.2 A Proof of Concept Prototype 53

5.4 Summary 56

6 Conclusions 596.1 Conclusion and Discussion 596.2 Recommendations for Further Research 61

References 63

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LIST OF FIGURES

Figure Title Page

2.1 Conceptual Web Services Model 102.2 Example of RDF Metadata Embedded in Web Page 142.3 Sample RDF Statements about Person 142.4 OWL-S Service Profile Description of GetSineValue Web Service 17

3.1 General Architecture of Support Systems 20

4.1 Blog, Digital Library, and Knowledge Conversion Modes 234.2 Blog+DL Framework for Computer-Aided Engineering Design 254.3 User-specific Initialization of Prototype 304.4 Blog Publication Features of Prototype 334.5 Blog Consumption Features of Prototype 354.6 Blog Socialization Features of Prototype 374.7 Knowledge Archiving Features of Prototype 384.8 Knowledge Retrieval Features of Prototype 41

5.1 Semantic Web Services Model 455.2 Semantic SOAP Message 465.3 Components of Semantic Web Service 465.4 System Configuration of Prototype 525.5 Windows Mobile User Interface 545.6 Sample Request Message from PDA Client 555.7 OWL Definition of ASTM A36 Steel 565.8 OWL Definition of Pinned Node 575.9 Output Screen on PDA Client 57

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LIST OF TABLES

Table Title Page

6.1 Features Comparison with Existing Work 60

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CHAPTER 1

INTRODUCTION

1.1 Motivation

Two obstacles that practicing structural engineers often face in a design projectare limitations in available computing tools and limited access to relevant knowledge.Several tasks are involved when a project is launched, in terms of research, discussionwith colleagues and computational workflow. These tasks are repeated over and overagain until a final design is arrived at. If they are not well facilitated, multiple bottle-necks will occur and an engineering firm suffers low productivity among its engineers.

Not all engineering firms can afford expensive design software packages, andthe software itself often does not fully address their needs. Some firms may share acommercial software package or use it on a pay-per-use basis; whereas others developsoftware or subroutines in-house. Some in-house tools may be so well-developed orhave such well-developed components that it is more productive for engineers to reusethem rather than to develop new systems from scratch. However, both commercialand in-house tools are typically based on specific assumptions that need to be satisfiedbeforehand, and their input and output data are usually based on specific conventionsand formats that must be strictly followed to obtain sensible results. As the numberof shared tools grows, it becomes difficult for users to find appropriate tools that fittheir design problems best, and it is tedious for them to manually adapt the potentiallyincompatible input and output data of the tools to a computational workflow, such asthe discretization–analysis–postprocessing process in finite element methods.

In terms of knowledge management, on the launch of a new project, file cabi-nets are often searched to retrieve reference documents of similar past projects. Forless common projects, significant time is typically invested in research to come up witha solution strategy with the assistance of senior engineers or experts. This processis time consuming and can become a bottleneck if not well facilitated. Many knowl-edge management solutions are available in the literature or as commercial softwarepackages. However, most of them aim single-sidedly to capture tacit knowledge fromexperts into a knowledge repository, rather than facilitating the sharing, discussingand developing of new knowledge among individuals, which are key practices for thesustainable improvement of knowledge in an organization according to the theory oforganizational knowledge creation (Nonaka and Takeuchi, 1995).

1.2 Problem Statement

This research aims to create a support system that improves the productivity ofstructural engineers during the engineering process and, particularly, to alleviate thetwo obstacles introduced earlier which hinder the performance of practicing engineers.The realization of a support system that alleviates productivity obstacles involves ad-dressing issues in the following areas:

1. Identification of the necessary features for a structural engineering support sys-tem;

2. Knowledge management best practice according to knowledge management the-ory; and

3. Effective methodologies for precise storage and retrieval of knowledge.

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4. Development and sharing of in-house software tools as well as general-purposesoftware packages;

5. Automated integration and execution of in-house and general-purpose softwaretools;

Knowledge and tools in structural engineering as well as well-established com-puter technologies, research, and standards are integrated in this study. The former arerequired to ensure that the obstacles specific to structural engineering are addressedand the latter are required to ensure the scalability and sustainability of the proposedsolution.

1.3 Objectives

The primary objective of this study is to develop a support system that improvesthe productivity of structural engineers during the engineering process. To fulfill thisprimary objective, the following secondary objectives are set:

1. To develop a framework and a prototype support system for structural engineersthat facilitates the improvement and mutual sharing of knowledge and skills inthe form of multimedia documents and computational tools. The frameworkincludes

(a) the identification of the features of a good knowledge management systembased on knowledge management best practice,

(b) a methodology to appropriately apply computing and information technolo-gies in the knowledge management context, and

(c) methods and enabling modules that allow knowledge and computationaltools to be created, shared and improved by semantic computing technolo-gies.

2. To develop a framework and a prototype system that enables the dissemina-tion and use of intelligently unified computational tools shared in the structuralengineering knowledge system. Specifically, these include

(a) a procedure for structural engineers to develop and/or disseminate in-houseand commercial computational tools,

(b) the data model and format requirements for structural engineering compu-tational tools to interoperate,

(c) the software services and components necessary to receive, interpret, andprocess client requests, and

(d) the decision processes, knowledge, and rules by which a software agentsearches, evaluates, selects, and executes computational tools in a fully au-tomated structural engineering workflow.

1.4 Scope and Research Approach

The scope of this study covers both knowledge management and computationalinteroperability in the structural engineering context. Beyond the literature review

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traditional for research of this type, the work is anchored in observations of real worldconstraints on practicing engineers in engineering consulting firms as experienced bythe author. This led to the development of a Web-based proof of concept knowl-edge management prototype as well as a set of software infrastructure for the sharingand interoperability of computational tools. The former facilitates the creating andsharing of knowledge in the form of multimedia files and computational services. Itallows users to exchange opinions about posted issues and services and to save someof them to personal knowledge portfolios for later reference. It also allows access toshared computational tools which can be invoked and interoperated by the softwareinfrastructure developed in this study. To demonstrate the full potential of this infras-tructure, a prototype is separately developed to illustrate an intelligent collaborationof many structural analysis software agents located on personal computers and parallelcomputer clusters on the Internet to solve problems in linear elasticity.

The implementation of the system prototypes is based primarily on open-sourcesoftware tools. Several widely-accepted semantic computing standards, such as XML,Web Services and Semantic Web standards sanctioned by the World Wide Web Con-sortium (W3C), have been used during the development of the frameworks and theprototypes. As the term “semantic” implies, artificial intelligence techniques are usedextensively.

1.5 Contributions

This study is aimed at improving the productivity of structural engineers inthe engineering process by dealing with computational workflow and knowledge man-agement issues using semantic computing. The results of this study are an intelligentstructural engineering support system and two underlying frameworks: one which cre-ates a collaborative environment where structural engineers, both novice and expert,can share and sustainably improve their knowledge and another which turns the Inter-net into a large platform for numerical analysis of structures where computational toolsindividually developed and located on heterogeneous computer platforms intelligentlycollaborate. It is expected that the application of the frameworks and support systemdeveloped and demonstrated in this study can alleviate computational and knowledgemanagement problems in project design and other engineering work and thus improvethe productivity of individual engineers and the competitiveness of engineering firms.

1.6 Organization

The rest of this dissertation is organized as follows: Chapter 2 gives backgroundinformation about the theory, technologies and research that pertain to the develop-ment of support systems for the engineering process. Chapter 3 describes commonobstacles in the structural engineering process, identifies necessary features and con-ceptualizes the general design for structural engineering support systems. Chapter 4presents the structural engineering support system using semantic computing and de-scribes the architectural framework on which it is based. Chapter 5 addresses theinteroperability of computing resources which enables the sharing of computationaltools via support systems. It proposes a framework for the interoperation of structuralengineering software modules and demonstrates the framework by a system prototypeand a case study. Chapter 6 concludes this dissertation and discusses the benefits andlimitations of the presented work, with recommendations for further research.

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CHAPTER 2

LITERATURE REVIEW

“Semantic Computing encompasses all aspects of computing where data is encoded,processed, stored or transferred using techniques that communicate the meaning of thedata in addition to the data itself” (Boakes, 2007). An overview of the theory, technolo-gies and research that pertain to the development of a support system for structuralengineering processes using semantic computing is presented in this chapter. First, thetheoretical background of knowledge management (KM) and two illustrative KM sys-tems for engineering design firms are presented. Weblogs, a socialization and knowledgesharing enabling technology for the Web, and Digital Libraries, a technology for fastand accurate archiving and retrieval of electronic documents and multimedia contentin organizations, are discussed next. Web Services technology, a modern paradigm fordistributed computing on the Internet is then presented. Finally, the Semantic Web, anemerging technology that brings artificial intelligence to the Web, and Semantic WebServices, the joint application of Web Services and the Semantic Web, are discussed.

2.1 Knowledge Management

2.1.1 Definition and Significance

Knowledge management is a process for optimizing the effective application ofintellectual assets to achieve organizational objectives (Pollock, 2001). It is “the disci-pline of creating a thriving work and learning environment that fosters the continuouscreation, aggregation, use and re-use of both organizational and personal knowledge inthe pursuit of new business value” (Cross, 1998, as cited in Quintas 2005). Knowledge,the sets of processed information in a relevant context ready for understanding andaction (Turban and Aronson, 2001, as cited in Mezher et al. 2005), is an organization-specific resource that is indispensable to create value for the organization (de Geytere,2007). Knowledge differs from data and information in that it is “a function of aparticular stance, perspective, or intention” and involves an action “to some end;”whereas information and data are the “necessary medium or materials for eliciting andconstructing knowledge.” Knowledge, data, and information however are alike in thatthey are “context specific” and “relational” (Nonaka and Takeuchi, 1995, p. 58). Indecision making terms, knowledge contains the least “noise” whereas data contains themost noise pertaining to a decision making process (Sheehan et al., 2005). Businessorganizations that manage the creation and the application of knowledge more effec-tively will be more competitive than others. Organizations that better manage andshare knowledge from past experiences of their peers will more effectively utilize theirresources to fulfill their mission. In the construction industry, which is very competi-tive with low profit margins, although each project is invariably to some extent unique,there are common processes which require engineers to seek out knowledgeable persons“who know what” and to learn the “lessons learned” from them in a timely fashionto minimize cost and stay competitive. This competitive environment has made KMattractive in engineering consultant firms (Carrillo and Chinowsky, 2006; Mezher et al.,2005). KM helps an engineering consulting firm to leverage their technical skills andstore them in a manner to speed up the work, and compete with others in such a waythat projects are produced with high quality in less time (Mezher et al., 2005). As theworld enters the “knowledge society,” the basic economic resource is no longer capital,

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natural resources, or labor, but is and will be knowledge; and “knowledge workers”will play an increasingly central role in society (Drucker, 1993; Nonaka and Takeuchi,1995).

2.1.2 Classification of Knowledge

Knowledge can be classified into two kinds, namely, explicit knowledge and tacitknowledge. Explicit knowledge can be articulated in formal language such as grammat-ical statements, mathematical expressions, and manuals, which can be processed by acomputer with relative ease. Tacit knowledge on the other hand consists of personalknowledge embedded in individual experience and involves intangible factors such aspersonal beliefs, perspectives, and value systems, which are difficult to articulate com-pletely in formal language. Because of its subjective and intuitive nature, it is difficultto process or transmit acquired tacit knowledge in a systematic or logical manner (Non-aka and Takeuchi, 1995). A large portion of human knowledge resides in the form oftacit knowledge, and often tacit knowledge is regarded as the more important kind ofknowledge (Nonaka and Takeuchi, 1995, p. viii), as may be illustrated by the quotes“If NASA wanted to go to the moon again, it would have to start from scratch, hav-ing lost not the data, but the human expertise that took it there last time” (Brownand Duguid, 2000, as cited in Quintas 2005) and “A master craftsman ... develops awealth of expertise at his fingertips after years of experience. But he is often unableto articulate the scientific or technical principles behind what he knows” (Nonaka andTakeuchi, 1995, p. 8).

2.1.3 Theory of Organizational Knowledge Creation

Knowledge is dynamic in nature. Nonaka and Takeuchi (1995) proposed thetheory of organizational knowledge creation which postulates four temporal modes ofconversion among tacit and explicit knowledge, namely, (1) socialization from tacitknowledge to tacit knowledge, (2) externalization from tacit knowledge to explicitknowledge, (3) combination from explicit knowledge to explicit knowledge, and (4) in-ternalization from explicit to tacit knowledge, which constitute the “knowledge spiral”that drives innovation in an organization.

Socialization, the process of “sharing experiences and thereby creating tacitknowledge” such as shared mental models and technical skills, helps an individualto acquire tacit knowledge directly from others through observation, imitation, andpractice in a field of interaction. Externalization, the process of “articulating tacitknowledge into explicit concepts,” is the definitive process through which an individ-ual attempts to conceptualize a mental image linguistically by metaphors, analogies,concepts, hypotheses, or models. It is typically used in the process of concept cre-ations and is “triggered” by meaningful dialog or collective reflection. Combination,the process of “systemizing concepts into a knowledge system,” involves “networking”different bodies of explicit knowledge in media such as documents or computerizedcommunication channels so that they are “crystallized” into a new piece of knowledge,product, service, or methodology. Formal education and training in schools usuallytakes the combination form of knowledge conversion. Internalization, the process of“embodying explicit knowledge into tacit knowledge,” is the process through whichexperiences of individuals, obtained through socialization, externalization, and combi-nation, are internalized into their tacit knowledge bases in the form of shared mentalmodels or technical know-how. Internalization is closely related to “learning by do-

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ing.” Documentation helps individuals to internalize what they experienced and alsofacilitates the transfer of explicit knowledge to other people.

Each mode of knowledge conversion naturally produces different knowledgetypes: socialization produces sympathized knowledge; externalization produces con-ceptual knowledge; combination produces systemic knowledge; and internalization pro-duces operational knowledge. These knowledge types interact with each other in a spiralfashion: sympathized knowledge may become explicit conceptual knowledge throughsocialization and externalization. The conceptual knowledge produced then becomes aguideline for creating systemic knowledge through combination. The systemic knowl-edge is next converted to operational knowledge through internalization. When anindividual socializes with colleagues and externalizes his knowledge, his operationalknowledge consequently triggers a new cycle of knowledge creation. Knowledge conver-sion is a social process between individuals and is not confined within an individual. Ashuman cognition is the deductive process of an individual, who is never isolated from so-cial interaction when things are perceived, the quality and quantity of tacit and explicitknowledge are constantly “amplified” through shifts between these social conversionprocesses. The four temporal modes of knowledge conversion among individual staffmembers, departments, and divisions in an organization therefore constitute a “knowl-edge spiral” that drives innovation in the organization. This knowledge creation model,based on the theory of organizational knowledge creation, is sometimes called theSocialization-Externalization-Combination-Internalization (SECI) model (de Geytere,2007).

2.1.4 Knowledge Management Process and Tools

The KM process has been discovered rather than invented. Human activity isinconceivable without knowledge. Even though the phrase “knowledge management”came into common usage in the mid-1990s, the management of knowledge processesbegan long before the term was coined, without the KM label (Quintas, 2005). KMis different in each organization. There is no one right way and organizations developdifferent approaches according to their values and objectives (Sheehan et al., 2005).Some KM processes that function well may not have the KM label affixed. In contrast“many so-called KM initiatives and tools that emerged in the late 1990s were lessconcerned with addressing real knowledge issues than informal or existing processesthat are not so labeled” (Quintas, 2005). The focus of these initiatives and toolswas often on capturing explicit knowledge, rather than facilitating the conversion andsharing of tacit and explicit knowledge, as in the SECI model. Their scope can thusmore properly be described as limited to “information management.”

2.2 Knowledge Management in Engineering Design Firms

Structural engineers often work for engineering design and consultant firmsrather than for construction managers or contractors. The nature of their work differsfrom that in construction management, in which project planning and scheduling, re-source allocation, procurement and document management are of more concern. Multi-ple KM initiatives and tools for engineering design firms are available in the literature,cf. Mezher et al. (2005); Jonathan Cohen & Associates (2004); Al-Ghassani et al.(2005); Carrillo and Chinowsky (2006). Most of them, however, rely on proprietarytechnologies that do not conform to Internet standards other than basic HTTP and

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HTML for Web publication. Cross-system interoperability and advanced applicationsof Web technologies, such as the Semantic Web (Berners-Lee et al., 2001) and Web feeds(Wikipedia, 2007e) cannot therefore be implemented without major modifications ofthese approaches. For illustrative purposes, the intranet knowledge management sys-tems at the consultant firms DAR, ADD Inc. and Ove Arup and Partners are presented.Weblogs and Digital Libraries, recent Internet technologies which overcome these diffi-culties and can be integrated to form part of a more effective knowledge managementsystem, are introduced in the next section.

2.2.1 DAR Knowledge Management System

Mezher et al. (2005) developed a KM system at the DAR consulting firm. Thesystem was developed based on the KM cycle outlined by Turban and Aronson (2001)which consists of six processes, namely, (1) the creation of knowledge from designprocedures prepared by engineers, lessons learned from construction sites and knowndesign hints and shortcuts, (2) the capturing of explicit and tacit knowledge createdby the first process, (3) the refining of knowledge through the review and approvalby group leaders and directors, (4) the storage of useful knowledge in a repositoryfor access by others in the organization, (5) the management of knowledge to main-tain relevance, accuracy and currency, and (6) the dissemination of knowledge in aformat useful to users. The system consists of shared folders on the department’s fileserver repository at the head office and a Web-based user interface that helps usersto access Word, Excel or AutoCAD documents and relevant e-mails in file server hi-erarchies. Folders on the file server include design standards and manuals, archives ofcompleted project drawings and documents, pending project drawings and documents,department-specific software and worksheets, libraries of AutoCAD shortcut menusand programs as well as document templates and forms, all of which go through thesix KM cycle processes above. Access to these folders is restricted to relevant personnel.The system is accessible from branch offices through the company’s intranet.

The system provides engineers with ready access to proper design templates andprocedures, as well as past site lessons. It prevents unnecessary re-work and allows en-gineers to work both faster and more efficiently. Although the knowledge retrievalprocess is assisted by a Web interface, it is not clear whether the knowledge creationand review workflow and repositorial publication, which are part of the externalizationprocess, are computerized or automated. Metadata, as well as keyword and full-textsearch features, which are information and tools necessary for precise retrieval of knowl-edge from large knowledge bases or nested folder hierarchies, are not mentioned. Thesystem is therefore likely to not be scalable. Users will also find it difficult to review,publish and precisely locate pieces of knowledge as the size of the knowledge base grows.In addition, the system does not support communication means other than e-mails toenable peer discussion and collaboration, leaving the socialization aspect of the SECImodel relatively unfacilitated.

2.2.2 ADD and Arup Intranet Systems

Jonathan Cohen & Associates (2004) developed intranet KM systems for ADDInc. and Ove Arup and Partners. ADD is a medium-sized multidisciplinary design firmwith 150 employees and three offices in Cambridge, Massachusetts, San Francisco andMiami; whereas Arup is a very large engineering consultant firm with 5,000 employeesin 60 offices and 40 countries.

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The ADD intranet is a unified Web portal to internal firm information. ADDstaff are directed to a “today” homepage when they enter the site where they arepresented with updated events, news and announcements stored on a shared calendardatabase. From the homepage, they can navigate to information about firm standardprocedures, document templates and CAD details. Meetings can be scheduled usingthe calendar function; and pictures and news items can be shared among colleaguesthrough personal pages assigned to each user. These personal pages help to create asocial network across the three offices. A list of experts within the firm is also postedon the intranet so that they can be promptly consulted should the need arise.

The Arup intranet is an attempt to create a road map to the knowledge withinthe firm, which is so large that it is difficult to keep track of all the expertise it possesses.The Arup intranet is a central repository of the firm’s expertise that provides firm-wide access to specifications in Word and PDF formats as well as CAD details with adatabase that tracks how specific details have been implemented in projects. Arup’sengineers can annotate their experience with the details and specifications, such as howthey worked in one project and failed in another, and so capture the feedback loopbetween design and implementation. These experience records and the feedback loopare invaluable information and practice that help distinguish Arup in the marketplace.Employees of Arup are also allowed to “volunteer” information through personal Webpages where they can quickly and easily share their interests and expertise with therest of the firm (Sheehan et al., 2005). In this way individuals can socialize and jointhe SECI knowledge spiral which drives innovation at the firm.

The ADD and Arup intranet systems are examples of systems that conform toKM best practice but rely on proprietary techniques. Cross-system interoperabilityand advanced applications of recent Web technologies, which can further improve theproductivity of engineers, cannot thus be implemented easily.

2.3 Weblogs and Digital Libraries

2.3.1 Weblogs

A weblog, or a blog , is a Web-based journal publication which consists primarilyof periodic articles, normally in reverse chronological order (Wikipedia, 2007b). Eacharticle in a blog is called a blog entry . A person who publishes to a blog is called ablogger . To publish to a blog is called to blog , the gerund form of which is blogging .The totality of all blogs on the Internet is called the blogosphere (Wikipedia, 2007c;Crystal, 2006).

A blog entry usually consists of (1) the title, (2) the content, (3) the categories,tag names, or keywords of the content, (4) the date and time of publication, (5) thecomment section where readers and the blogger can discuss and exchange experiencesand opinions about issues raised in the blog entry, and (6) the trackback (Trott andTrott, 2002) hyperlink where the readers who blog about the blog entry can notifythe original blogger about the existence and the content of their blogs. Hyperlinks,comments, trackbacks, and citations in blogs create online social networks and promotethe externalization, socialization, and combinations of knowledge among the bloggers,according to the SECI model.

Blogging is as easy as composing an e-mail. A blogger can blog about anything,ranging from news items, personal opinions, pictures, hobbies, problems at work, torandom thoughts. A blog entry usually contains some hyperlinks to other Web docu-

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ments which are the subjects of discussion. To publish a blog entry, a blogger logs onto his account on the blog service provider Website, cf. Blogger (2007) and WordPress(2007), clicks the compose button, types the content into a Web form, enters the titleand keywords for the content. Multimedia contents such as pictures, audio/video clips,as well as Word and Excel files can be uploaded and included as attachments. When apublish button is clicked, the blog entry is instantly published to the Internet and canbe accessed by friends, colleagues, and the public through regular Web browsers, Websearch engines, and dedicated blog reader software which conforms to the Really Sim-ple Syndication (RSS) (RSS Advisory Board, 2006) or Atom (Nottingham and Sayre,2005) Web syndication (a.k.a. Web feed) standards. A blogger can also send e-mailsto a secret e-mail address to publish the contents and picture attachments directlyfrom an e-mail client software on a laptop computer or a mobile phone. Blogging thusallows people to share information on the Internet with very few barriers. Technorati,Inc., a Web tracking company, reported that as of April 2007 there were 70 millionsblogs on the Internet, with 120,000 new blogs added each day, and 1.5 million posts perday. The blogosphere grew from 35 to 70 million blogs in 320 days. Japanese, English,Chinese, and Italian are the four major blogging languages—37 percent of the blogsare in Japanese, 33 percent in English, 8 percent in Chinese, and 3 percent in Italian(Sifry, 2007).

2.3.2 Digital Libraries

A digital library is an integrated set of services for capturing, cataloging, stor-ing, searching, protecting, and retrieving information (Reddy et al., 1999). It comprisesfocused collections of digital objects, including text, video, and audio, along with meth-ods for access and retrieval, and for selection, organization, and maintenance of thecollections (Witten and Bainbridge, 2003), all of which support life-long learning, re-search, scholarly communication and preservation (Wikipedia, 2007d). Digital librarieswere originally used to archive the digitized copies of rare documents, books, and thepictures of historical objects so that they can be studied by people of later generations.They have also recently been used as central repositories to preserve the works of in-dividuals in an organization, so that they do not vanish with time and technologicalobsolescence (DSpace, 2007b).

A digital library system is often developed such that it is accessible on theWeb, with the user interface resembling that of a Website. However, not all Websites,even the ones that offer focused collections of well-organized material and appropriatemethods of access and retrieval, can be regarded as digital libraries, unless metadata(data about data), which are important information to precisely catalog, locate andretrieve pieces of information, are stored along with each object in the collections,and the access, retrieval, and modification of metadata, as well as the retrieval ofobjects based on them, are facilitated by the system (Witten and Bainbridge, 2003).The metadata typically used in digital libraries include bibliographic information andsubject keywords, much as the indexing information used in physical libraries.

The users of a digital library system have one of two roles, namely, the publisherand the reader. In some digital library implementations, such as the Greenstone Dig-ital Library software (Witten et al., 2006), a librarian takes the sole publisher role tocapture (e.g., by scanning printed articles or typing in a word-processor), create cata-loging metadata, store, manage, and publish the collections of digital objects; and thegeneral users take the reader role to browse, search, and consume information in the

9

Service Registry

Service Consumer

Service Provider

Find

Publish

Bind

Service Description

Web ServiceSOAP

WSDL

UDDI

Figure 2.1: Conceptual Web Services Model

collections. In other digital library implementations, such as DSpace (MIT Librariesand Hewlett-Packard Company, 2007) which is based on an institutional repositoryconcept, select users may also publish to some collections in the repository, thus takingboth the publisher and the reader roles. Computers and users can locate objects acrossheterogeneous digital library systems by using standard query protocols, such as theOpen Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) (Lagoze et al.,2004). When a digital library system is deployed in an organization, it functions as aknowledge base which stores explicit and externalized knowledge from the users. Themetadata-rich search feature of the system also allows users to precisely and promptlylocate pieces of knowledge which can be used to solve their specific problems in a timelyfashion. In this sense, a digital library system can thus promote the combination andinternalization of knowledge in the SECI model.

2.4 Web Services

Web Services (W3C, 2004d) is a modern paradigm of distributed computingthat uses the Internet as the communication medium. Its fundamental concept is tobuild computer software by making use of remote procedure calls (RPC) over the In-ternet or an intranet. It differs from other distributed computing technologies, forexample CORBA (OMG, 2005) and DCOM (Microsoft, 1996), in the use of platform-independent standards, based on HTTP (Fielding et al., 1999) and Extensible MarkupLanguage (XML) (Fallside, 2001), which allows service providers to hide the imple-mentation details from clients. The clients need to know the URLs (Berners-Lee et al.,1994) of the services and the data types for method calls, but not the details of theservice implementation in order to use them (Basha et al., 2002). Web Services is dif-ferent from the common term “web services” (spelled in lower case) adopted by someresearchers, which simply refers to the “ability to access many services through theInternet” (Chen et al., 2006) via an arbitrary communication protocol without regardto the specific World Wide Web Consortium (W3C) standards that are introduced inthis section.

The typical architecture of Web Services is shown in Figure 2.1. Three roles,namely, a service provider, a service consumer, and a service registry, and three oper-ations, namely, publishing, finding, and binding, are involved.

A service provider is an entity that creates Web services. Typically, it exposes

10

certain functionality in their organization as a Web service that is made available forany other organization to invoke. To be effective, a Web service needs to performtwo tasks. First, it needs to describe the Web service in a standard format thatis understandable by all organizations that will be using that Web service. Next itneeds to publish the details about its Web service in a central registry that is publiclyaccessible.

A service consumer is any organization that uses Web services provided bya service provider. It knows the functionality of a particular Web service from thedescription made available by the service provider. To retrieve those details, a serviceconsumer searches the registry where the service provider has published its Web servicedescription. The service consumer can then obtain the description of the requiredmechanism, bind to the service provider’s Web service and invoke that Web service.

A service registry is a central location where service providers list their Webservices, and where service consumers search for them. Service providers usually pub-lish their Web service capabilities in the service registry for service consumers to findand later, if interested in the provided service, bind to them. Examples of informationtypically stored in the service registry include the detailed description of an organiza-tion, the Web services that it provides, and a written description of each Web serviceas well as the input and output formats for its invocation.

Web services take multiple roles on different occasions. A Web service takesthe “provider” role when it accepts requests from consumers to be processed. It takesthe “consumer” role when it delegates its tasks to other Web services. A Web serviceprovider takes the special role of “service registry” if it helps a service consumer todiscover a service provider for later binding. A client program that interacts onlywith end users takes the consumer role when it requests a service from a Web serviceprovider.

The Web Services architecture aims to achieve interapplication communication,irrespective of the programming language that the application is written in or theplatform that the application is running on, by three fundamental operations whichare “finding,” “binding,” and “publishing.” To make this happen, standards for eachof these three operations and a standard way for a service provider to describe theirWeb services are needed.

The Web Service Description Language (WSDL) (W3C, 2001) is such a standardthat uses the XML data format to describe Web services. The WSDL document for aWeb service defines the methods that are present in the Web service, the input/outputparameters for each method, the data types, the network transport protocol used, andthe URLs where requests for services are to be submitted and from which the resultscan be received.

The Universal Description, Discovery, and Integration (UDDI) standard (OA-SIS, 2002) provides a way for service providers to publish details about their organi-zation and the Web services that they provide to a central registry. It also provides astandard for service consumers to find service providers and details about their Webservices. Publication of the details is the “description” part of UDDI and the locationof such details is the “discovery” part of it. Publication of these details is typicallyachieved by advertising hyperlinks to their WSDL documents on UDDI service reg-istries.

Communication between service consumers, service providers and service reg-istries is through the Simple Object Access Protocol (SOAP) (W3C, 2000), which is a

11

lightweight XML communication mechanism to exchange information between appli-cations, regardless of the operating systems, programming languages, or object modelsemployed in their development. SOAP may be visualized as a “carrier” of data encodedin XML format.

Web Services technology has been involved in many areas of scientific comput-ing, ranging from computational infrastructure developments (Chiu et al., 2002; vanEngelen, 2003) to finite-element analysis of a coupled fluid, thermal, and mechani-cal fracture problem (Chew et al., 2003). One significant effort in this area is thestandardization attempt by the Global Grid Forum (GGF, 2003) to create the OpenGrid Service Architecture specification (OGSA) (Foster and Gannon, 2003) which isthe global standard for interoperation in the grid computing community (Foster, 2002;Foster et al., 2001) and is the specification in which the SOAP and WSDL standards,two important components of Web Services architecture, have been adopted. As thereare already a significant number of scientific computing projects that rely on grid com-puting infrastructure, such as NASA’s Information Power Grid (IPG, 2003) and theTeraGrid (Catlett, 2002), imposition of such a standard is likely to increase the numberof scientific computing applications that rely on Web Services technology significantly.

2.5 The Semantic Web

2.5.1 Background

The Semantic Web (Berners-Lee et al., 2001) is a vision for the next generationof the Web on which information will be useful and meaningful not only for peoplebut also for computers. Computers will be able to understand pieces of informationon Web pages rather than merely presenting them to users, and will thus be able toautonomously assist users in manipulating the information.

The Semantic Web is not a separate Web. Rather, it is an evolution of thepresent Web into one in which information is given well-defined meaning (Berners-Leeet al., 2001). Documents on the present Web can be transformed into Semantic Webdocuments by augmenting them with metadata aimed at computers. Metadata is dataabout other data that enables computers to determine the meaning of informationin a Web document by following hyperlinks to definitions of key terms and rules forreasoning about them logically.

The Semantic Web relies on two existing technologies which are XML and theResource Description Framework (RDF) (Berners-Lee et al., 2001).

XML (Fallside, 2001), a generalization of HTML (W3C, 1999a), has been pro-posed as the language for the publication of Web documents. XML allows Web pub-lishers to add structure to their documents by using tags, which are hidden labels thatannotate Web pages or sections of text on a page. Computer programs can make use ofthese tags in many ways, from rendering XML documents in a human-friendly formaton Web browsers, to mapping XML documents as data sources. However, XML tagsare not uniformly created and they can be used by different groups of people. There-fore, their intended meaning must be precisely comprehended by the programmers inorder to develop programs that properly manipulate the XML tags. The intendedmeaning of an XML tag will be understood if the name of the tag is drawn from acommon dictionary.

RDF (W3C, 1999b) is a language for describing information about resourceson the Web. Typically encoded in XML format itself, the RDF language allows Web

12

publishers to assert that particular things (Web pages, videos, etc.) have properties(such as “is authored by” or “is a kind of”) with certain values (such as a thing,a person, etc.), in a grammatical form similar to the subject, verb, and object ofan elementary sentence (Berners-Lee et al., 2001). An assertion that the Web page“www.asce.org” “is authored by” “ASCE” is called an RDF statement, or a triple. Aset of triples with the same term or concept in a domain forms a definition of the term.A collection of the definitions and the relations among terms in a domain constitutesan ontology of the domain. In the context of the Semantic Web, an ontology is adocument or file that formally defines terms and their relations (Berners-Lee et al.,2001). It can serve as a common dictionary from which XML tag names, and RDFproperty names and values can be drawn.

Subjects, verbs, and objects in RDF statements are uniquely identified by Uni-versal Resource Identifiers (URIs). The Uniform Resource Locators (URLs) that linkWeb pages are the most common type of URI. URIs ensure that terms or concepts arenot just words in a document but are tied to a unique definition that anyone can findon the Web (Berners-Lee et al., 2001). In addition, the object of an RDF statementcan be another RDF statement, so that complex RDF statements can have other RDFstatements nested inside in multiple layers. Figure 2.2 illustrates the RDF statementsserving as metadata about the Web page http://ce-res.blogspot.com/2005/10/

kmitlsut-reinforced-concrete-design.html. It asserts that the page “has title”“Reinforced Concrete Design Worksheets,” is categorized in the “subjects” “RC De-sign,” “Working Stress Design,” etc., and “is authored by” the person identified by theURI http://stweb.ait.ac.th/~st029284/foaf.rdf#thitiv.

Ontologies and RDF statements are typically stored on Web servers for laterretrieval by other parties in the same way as the publication of traditional Web doc-uments. The statements about the author of the Reinforced Concrete Design Work-sheet can be retrieved for display on a Web browser by accessing the URI http:

//stweb.ait.ac.th/~st029284/foaf.rdf#thitiv. It is also possible to embed anRDF statement that employs terms from specific ontologies inside hidden tags of Webdocuments themselves (Palmer, 2002). Software tools such as the Jena Semantic WebFramework (Jena, 2007) provide a means to retrieve ontologies and RDF statementsfrom Web sources and store them in a local knowledge base. The set of inferencerules that correspond to the semantics of the RDF constructs (W3C, 2004c) is prebuiltinto Jena. Answers to queries such as “give me a list of Web pages authored byhttp://stweb.ait.ac.th/~st029284/foaf.rdf#thitiv” can be obtained throughJena’s Application Programming Interfaces (APIs) (Wikipedia, 2007a).

2.5.2 How the Semantic Web Works

The following examples illustrate how Web documents, XML, ontologies, andRDF statements work on the Semantic Web.

Example 1

John Doe is at the Reinforced Concrete Design Worksheet Web page on the CEResources Website (Vacharasintopchai, 2005). He is interested in the link mentioned onthe page and wants to contact the author for more information about it. The author’se-mail address or contact information is not given on the Web page. How can Johnfind the author’s e-mail address?

Traditional Solution. On the Web page, John has to find the name of the author.

13

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"

xmlns:dc="http://purl.org/dc/elements/1.1/">

< rdf:Description

rdf:about="http://ce-res.blogspot.com/2005/10/kmitlsut-reinforced-concrete-design.html"

5 dc:title="Reinforced Concrete Design Worksheets">

<dc:subject rdf:resource="http://thitiv.blogspot.com/semblog/terms.owl#rc-design"/>

<dc:subject rdf:resource="http://thitiv.blogspot.com/semblog/terms.owl#working-stress-design"/>

<dc:subject rdf:resource="http://thitiv.blogspot.com/semblog/terms.owl#ultimate-strength-design"/>

<dc:subject rdf:resource="http://thitiv.blogspot.com/semblog/terms.owl#structural-members"/>

10 <dc:language rdf:resource="http://www.iso.org/iso/en/prods-services/iso3166ma/02iso-3166-code-

lists/list-en1.html#TH"/>

</ rdf:Description >

</rdf:RDF>

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"

15 xmlns:dc="http://purl.org/dc/elements/1.1/">

< rdf:Description

rdf:about="http://ce-res.blogspot.com/2005/10/kmitlsut-reinforced-concrete-design.html">

<dc:author rdf:resource="http://stweb.ait.ac.th/~st029284/foaf.rdf#thitiv"/>

</ rdf:Description >

20 </rdf:RDF>

Figure 2.2: Example of RDF Metadata Embedded in Web Page

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"

xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"

xmlns:foaf="http://xmlns.com/foaf/0.1/">

5 <foaf:PersonalProfileDocument

rdf:about="http://stweb.ait.ac.th/~st029284/foaf.rdf">

<foaf:maker rdf:resource="#thitiv"/>

<foaf:primaryTopic rdf:resource="#thitiv"/>

</foaf:PersonalProfileDocument>

10

<foaf:Person rdf:ID="thitiv">

<foaf:givenname>Thiti</foaf:givenname>

<foaf:family_name>Vacharasintopchai</foaf:family_name>

<foaf:mbox rdf:resource="mailto:thitiv[at]gmail.com"/>

15 <foaf:homepage rdf:resource="http://thitiv.blogspot.com"/>

</foaf:Person>

</rdf:RDF>

Figure 2.3: Sample RDF Statements about Person

Then he has to go to a search engine, such as Google, and type in the author’sname and a keyword such as “e-mail” to search for traces of an e-mail address.If John is lucky enough, he gets the e-mail address after the first search.

Semantic Web Solution. A set of RDF statements that describe the Web page (Fig-ure 2.2) will usually be published in conjunction with the page content. A Se-mantic Web-enabled Web browser will recognize the “is authored by” RDF state-ment (the dc:author and the rdf:resource tags) and will be directed to theRDF statements about the author. The Web browser can inspect the statementsabout the author (Figure 2.3) and extract the author’s e-mail address from thefoaf:mbox statement automatically. This e-mail address will be suggested bythe Web browser as the e-mail address that John is looking for.

14

Example 2

The RC design Web page is categorized on the Website under a keyword “struc-tural members.” If John wants to see more articles about “structural members” fromthe same author, how would he do it?

Traditional Solution. On the Web page, the author provides a link to an index ofarticles in the “structural members” category. If John clicks on the “structuralmembers” link, he is directed to a list of articles about “structural members.”Had the author not provided such an index, John would have to search for a listof articles with “structural members” and the author’s name as the keywords.However, John is not likely to encounter an article about “beams,” althoughobviously it is a kind of structural member, unless “beams” is also explicitlyadded to the keyword list.

Semantic Web Solution. The Web browser will recognize the “subject” statements(the dc:subject tag) in the set of RDF statements about the RC design Webpage (Figure 2.2). When requested to find more Web pages whose dc:subject

property is http://thitiv.blogspot.com/semblog/terms.owl#structural-membersand whose dc:author property is http://stweb.ait.ac.th/~st029284/foaf.rdf#thitiv, the Web browser can browse through a list of RDF statements,then select the Web pages whose dc:author and dc:subject are the same as,or related to, the given ones, and then present the result accordingly. If the Webbrowser finds an RDF statement which asserts that http://thitiv.blogspot.com/semblog/terms.owl#beams is a “subclass of” (W3C, 2003) http://thitiv.blogspot.com/semblog/terms.owl#structural-members, it will also includethe article about “beams” in the results, as it can be deduced by an inferenceengine that beams are a type of structural member. In technical terms, we saythat the concept “structural members” subsumes the concept “beams.”

It should be noted here that the term “RDF statement,” used earlier, collectivelyrefers to the statements that are represented by the RDF language (W3C, 2003) as wellas the Web Ontology Language (OWL) (W3C, 2004a), which is an extension of the RDFlanguage for more comprehensive representation of knowledge on the Semantic Web.OWL provides several additional constructs, such as “equivalent class,” “intersectionof,” “union of,” “inverse of,” and “disjoint with,” that more precisely capture bodiesof knowledge over those available in the RDF language. Henceforth, the term “OWLstatement” may be used when it is desirable to emphasize the employment of OWL’sadditional constructs in the statement.

2.6 Semantic Web Services

2.6.1 Background

Web Services introduced a new model of distributed computing that uses theInternet as the communication platform. The present technology based on WSDL,SOAP, and UDDI allows automatic publication, discovery, and execution of services atthe syntactic level. However, WSDL documents, which play a key role in the interop-eration of Web services, are XML documents and therefore inherit the same semanticproblem of XML discussed earlier. To make proper use of the Web services advertisedby WSDL documents, programmers have to know in advance the intended meaning

15

of the custom tags that specify the input and output schemas as well as the names ofservices that a Web service provides. Like other XML documents, WSDL documentsare usually created and used by different groups of people. Unless supported by on-tologies, it would be difficult for a computer program looking for a GetSineValue Webservice that calculates the sine of a “degree” angle to autonomously execute and getthe correct sine value from an advertised TrigonometricSine Web service that takes“radian” angles as the input, even though both of them are described as Web servicesthat calculate the sine values of angles.

Semantic Web Services, the idea of augmenting Web service descriptions usingSemantic Web technology (Burstein et al., 2005), were introduced to address this kindof problem and to facilitate the autonomous publication, discovery, and execution ofservices at the semantic level. Semantic Web service description languages, such asOWL-S (OWL–S, 2003) and Web Service Modeling Ontology (WSMO) (Roman et al.,2005), were proposed as abstractions of syntactic Web service description languagessuch as WSDL, which are used in current Web Services technology. They are meantto be used with semantic matchmakers, which are the software agents that accept andkeep track of the descriptions of available services from providers and match themagainst the requirements from service consumers (Burstein et al., 2005). Matchmakersenhance the role of UDDI service registries in the Semantic Web Services architecture.

OWL-S is among the most widely used semantic Web service description lan-guages and was submitted to W3C for possible standardization (Martin et al., 2004).It describes the categories, the inputs, the outputs, and the consequences of Web ser-vices in terms of concepts defined in OWL ontologies. It also provides the groundingconstructs for specialization into WSDL constructs for compatibility with existing Webservices, which are described by WSDL documents. OWL-S is used as the semanticWeb service description language in this research.

2.6.2 How Semantic Web Services Work

To illustrate how Semantic Web Services can help a program looking for theGetSineValue Web service to get the correct value of sine from the TrigonometricSineWeb service, consider the OWL-S description of GetSineValue in Figure 2.4. The“service profile” description, which is the proposed information for use by matchmakers(Sycara et al., 2003), is presented. Figure 2.4 shows that:

1. The GetSineValueService (which is the service that the program is looking for)is a kind of SineFunctionEvaluator defined in the profileHierarchy taxon-omy;

2. The service takes one input parameter “Angular Degree” the definition of which isavailable at http://std.swscm.org/200407/quantities.owl#AngularDegree,and gives one output the definition of which is available at http://std.swscm.org/200407/quantities.owl#UnaryConstantFunctionQuantity; and

3. The effect of this service is defined by http://std.swscm.org/200407/Math-

OperationEffects.owl#TrigonometricFunctionValueEffect.

In the Semantic Web Services architecture, when a service consumer needs aservice from a provider, it creates an ideal service profile, such as the one in Figure 2.4,and submits it to a matchmaker as a request for recommendation (Sycara et al., 2003).

16

<rdf:RDF>

<service:Service rdf:ID="GetSineValueService">

<service:presents rdf:resource="#GetSineValueServiceProfile"/>

</service:Service>

5

<profileHierarchy:SineFunctionEvaluator rdf:ID="GetSineValueServiceProfile">

<service:isPresentedBy rdf:resource="#GetSineValueService"/>

<profile:hasInput>

10 <process:Input>

<process:parameterType

rdf:resource ="http://std.swscm.org/200407/quantities.owl#AngularDegree"/>

</process:Input>

</profile:hasInput>

15

<profile:hasEffect rdf:resource=

"http://std.swscm.org/200407/MathOperationEffects.owl#TrigonometricFunctionValueEffect"/>

<profile:hasOutput>

20 <process:UnConditionalOutput>

<process:parameterType

rdf:resource ="http://std.swscm.org/200407/quantities.owl#UnaryConstantFunctionQuantity"/>

</process:UnConditionalOutput>

</profile:hasOutput>

25

</profileHierarchy:SineFunctionEvaluator>

</rdf:RDF>

Figure 2.4: OWL-S Service Profile Description of GetSineValue Web Service

From the profiles registered by various service providers, the matchmaker selects theone that most closely matches the ideal profile requested, and recommends it to theconsumer for further binding between the two parties.

As the matchmaker processes the request, suppose that it had searched the listof service profiles advertised by service providers but could not find any service thatmatched perfectly. However, there is a TrigonometricSine Web service that advertisesitself as a kind of SineFunctionEvaluator which gives a UnaryConstantFunction-

Quantity output and the TrigonometricFunctionValueEffect effect. The servicetakes the input parameter defined by http://www.math.org/terms.owl#RadianAngle

rather than the http://std.swscm.org/200407/quantities.owl#AngularDegree. Ifthere are some assertions that the “radian angle” and the “angular degree” conceptsare related in some ways, for example:

1. Both “radian angle” and “angular degree” are subclasses of http://www.math.org/terms.owl#PlaneAngle; and

2. The “angular degree” “has conversion factor” of π/180 and ”has reference unitof measurement” of http://www.math.org/terms.owl#RadianAngle,

by populating these assertions into the knowledge base of an inference engine, andby querying whether the pairs of service types, input parameters, output parameters,and effects, subsume each other (Sycara et al., 2003), the matchmaker will be able todetermine that the two Web services near perfectly match, and could thus recommendthis TrigonometricSine Web service to the consumer. The consumer will be able touse the “has conversion factor” assertion to convert its angular degree into the radianangle unit expected by the service provider, and will be able to initiate a request tothe TrigonometricSine Web service and finally get the correct sine function value.

17

CHAPTER 3

DEVELOPMENT OF A SUPPORT SYSTEM FOR

STRUCTURAL ENGINEERING

3.1 Structural Engineers and Construction Projects

Construction projects start when owners decide to have buildings constructed tosatisfy their needs. Through bidding processes or fast track approaches, an architect iscontracted, the owner’s requirements collected and a preliminary design agreed upon.A series of drawings and specifications which consist primarily of architectural andstructural work are indispensable for the materialization of a project. Tender drawingsand specifications are required for bidding to select a general contractor for the project.Detailed drawings from designers and shop drawings from field engineers are necessaryfor work at the construction site to proceed.

Structural engineers play a critical role in all these construction project phases.At the outset, architects need a structural engineers’ opinion about viable structuralsystems that are appropriate for preliminary designs. Structural engineers are con-sulted for the estimated size of columns, beams and slabs before architectural drawingsare developed for tender. Detailed foundation designs are often the first constructiondrawings requested from structural engineers, even though the information for themis last obtained in the analysis and design process. In addition, field engineers needprompt advice from structural designers when unexpected difficulties occur at the con-struction site to minimize expenses from work delays.

Sound structural analysis and design is central to the safety and economy of aconstruction project; yet structural engineers are often on the critical path with tightschedules and are allocated very limited time for their tasks. Two obstacles that hinderthe productivity of structural engineers in dealing with their tasks are limitations inavailable computing tools and limited access to relevant knowledge. A support systemthat improves the productivity of structural engineers during the engineering processis therefore highly advantageous, as postulated in the first chapter.

3.2 Design Principles

Structural engineering departments typically have both senior engineers withextensive practical experience and junior engineers (or fresh graduates) with little pro-fessional experience. Many kinds of tools are used in the engineering process. Juniorengineers are often more acquainted with relatively new technologies, whereas seniorengineers have a wealth of practical insight and often know useful shortcuts. Somecommercial software packages are licensed and many departments have special-purposesoftware tools that have been individually developed in-house. Commercial softwareand in-house tools are usually shared among engineers in the department. The outputfrom structural engineers is commonly in the form of hand sketches or CAD drawings,supported by calculation sheets. On completion of projects, relevant documents arecollected on file servers or in cabinets for future reference.

To improve the productivity of structural engineers in dealing with their tasks,good support systems should at least have the following features:

1. References and Premises Engineering is an applied science by nature. It dealswith the application of pure scientific knowledge to solve practical problems.When structural engineers are assigned tasks at the launch of new projects, ref-

18

erence documents, such as design manuals, theoretical notes and data sheets, aswell as examples from similar past projects, are the first information needed tocome up with solution strategies. Good support systems for structural engineersshould thus facilitate the efficient storage, as well as fast and precise retrieval, ofreference documents and premises to minimize the engineers’ start-up time.

2. Knowledge and Tools Sharing Engineers study project requirements and pre-dict assumptions to simulate the behavior of structures under critical usage con-ditions. Based on the mechanical properties of construction materials, structuralmembers are designed such that they can withstand severe conditions at reason-able levels of safety. To maximize the safety and economy of structural engineer-ing products and arrive at proper rather than over- or under-designs engineersshould have access to the best possible set of knowledge and tools relevant to theirtasks. Methodologies, tips and techniques, as well as software tools licensed fromcommercial vendors or developed in-house, should be accessible to as many fel-low colleagues as possible. Good support systems for structural engineers shouldsecond facilitate the creating, sharing and disseminating of knowledge and toolsamong structural engineers so that they are well equipped for their work.

3. Computational Workflow Commercial and in-house software tools are typicallybased on specific assumptions that need to be satisfied. Their input and outputdata are usually based on specific conventions, formats and units of measurementthat must be strictly followed to obtain sensible results. Such software is oftenchained into a computational workflow to address specific requirements unsup-ported by general-purposes software packages. It is tedious and error-prone forthe engineers to adapt the potentially incompatible input and output data oftools in the chain manually. It is also difficult for users in workplaces where nu-merous software tools are shared to find the tools that fit their specific problembest. Good support systems for structural engineers should thus thirdly facilitatethe interoperation of software tools and allow them to be chained such that theleast possible user intervention is required to use them effectively and preventblunders.

4. Discussion and Collaboration Engineering is collaborative in nature. Seniorengineers in structural engineering departments have a wealth of practical insightand experience, whereas junior engineers have fewer but are more acquaintedwith modern technologies and design techniques. In general, the two exchangeknowledge and learn from each other. Senior engineers give guidelines and opin-ions about approaches to a solution. Colleagues give suggestions about toolsand techniques appropriate for specific project types. Although members of en-gineering teams usually meet and discuss formally on a regular basis, humanknowledge is amplified during informal socialization, as postulated by the theoryof organizational knowledge creation in Chapter 2. To assist structural engineersin exchanging and amplifying their knowledge in other than lunch and hallwaymeetings, good support systems for structural engineers should fourthly facilitateinformal discussion and collaboration among peers. The systems should allow ex-changes of audio/video clips, worksheets, pictures and drawings for effective peercommunication. To encourage user participation, the tool for discussion and col-laboration should be easy to use. The systems should also have an archiving

19

Figure 3.1: General Architecture of Support Systems

feature to make both past projects and the group thinking processes behind thesolutions arrived at, accessible to colleague engineers in the future.

5. Accessibility and Interoperability Support systems are not useful if they aredifficult to access. They will not wide gain acceptance and adoption in theindustry if they lack interoperability with other systems. In addition to the fourfeatures above, good support systems for structural engineers should thereforefollow open standards and be platform neutral for cross-system interoperabilityand accessibility. Users on different computing platforms (such as Windows, MacOS and Linux) should be able to access the systems indifferently. Ubiquitousaccess to the systems, especially from remote construction sites, would also beadvantageous for timely access to knowledge and tools to assist with unforeseenproblems on-site.

3.3 General System Architecture

The semantic computing technologies reviewed in Chapter 2 provide variousfunctionalities which can be combined to meet the five requirements for good struc-tural engineering support systems. This section proposes a general system architecturewhich utilizes semantic computing technologies to build such a system. The systemarchitecture consists of five components as depicted in Figure 3.1. Each component islabeled with numbers (1), (2), . . . , (5), which correspond to item numbers of the designprinciples. For example, (1) refers to References and Premises and (2) refers to Knowl-edge and Tools Sharing. The relationships between these components and the designprinciples can be explained as follows:

Digital Library The need for support systems to facilitate efficient storage and fast,precise retrieval of premises and reference documents matches well with the meta-data rich content archiving and retrieval features of digital libraries. Digital li-braries are specifically developed to store large amounts of electronic documentsfor archival purposes and to allow them to be precisely located and retrievedwith relative ease. Digital Library technology is therefore appropriate for theReferences and Premises requirement. The Web access and conformance to stan-dard query protocols of digital library systems make them appropriate for theAccessibility and Interoperability requirement.

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Weblogs The need for support systems to facilitate the creating, sharing, and dis-seminating of knowledge and tools matches well with the publication and socialnetworking features of Weblogs. Blogging allows engineers to instantly documentand disseminate among peers new ideas, software tools and techniques, and toconveniently receive focused feedback on particular topics in the form of com-ments and trackbacks. Engineers can also blog about work plans and difficultiesarising in particular projects to solicit comments and suggestions from colleagues.Weblogs, in this regard, are therefore appropriate both for the Knowledge andTools Sharing and the Discussion and Collaboration requirements. The confor-mance of Weblog servers to Web feed standards and the convenient means ofpublication offered by Weblogs make the technology especially appropriate forsupport systems, from the Accessibility and Interoperability point of view. Inaddition, the bibliographic information of blog entries is important metadatawhich contributes to the fast and precise retrieval of knowledge in blog content,according to the References and Premises principle.

Web Services The need for support systems to facilitate with the least user inter-vention the interoperation and chaining of software tools matches well with thecapability of Web Services and Semantic Web Services to help unify and utilizescattered computing resources. Web service providers and consumers include,but are not limited to, software tools on parallel computer clusters, personalcomputers, laptops and smartphone devices. With premises from service meta-data (i.e. service descriptions) and ontologies, the artificial intelligence inherentin Semantic Web Services technology also helps to intelligently discover and unifyheterogeneous computational services and to reduce the necessity for user inter-vention during workflow processes. Web Services and Semantic Web Servicestechnologies, in this regard, are thus particularly appropriate for structural engi-neering support systems, in terms of Computational Workflow, Accessibility andInteroperability, as well as Reference and Premises.

3.4 Summary

This chapter has described common obstacles in the structural engineering pro-cess and proposed five principles for the design of support systems to assist engineersin dealing with their tasks more productively. Semantic computing technologies wereidentified as enabling tools for such systems and a general architecture for the develop-ment of structural engineering support systems has been proposed. The methodologyto create support systems from these technologies are presented in the next two chap-ters, along with proof of concept prototypes. Metadata and ontologies as well as theirannotation in data and knowledge are central to all aspects of the development, hencethe term “semantic computing.” Their importance is illustrated throughout.

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CHAPTER 4

A STRUCTURAL ENGINEERING SUPPORT SYSTEM USING

SEMANTIC COMPUTING

4.1 Introduction

Weblogs (a.k.a. Blogs) and Digital Libraries, information technologies deploy-able on the Internet or an intranet, can be combined to build a new generation ofknowledge management (KM) systems, which can enhance the access to knowledgeand expertise of engineers, as previously postulated. Such systems assist them insharing tacit knowledge, developing concepts from many pieces of tacit knowledge,combining various elements of explicit knowledge, and storing new knowledge in theknowledge bases of individuals and the organization. In such a KM system, an engineeris assigned a personal blog where he/she can store work in progress, including collec-tions of random thoughts, ideas, documents, spreadsheets, or audio-video recordings.Through the dynamic social networking facilities of blogs, exchanges of comments anddiscussions among colleagues are electronically facilitated and individual blog entriesand discussions can be cross-referenced. After a work-in-progress is completed, relatedblog entries and discussions, as well as a project summary document, can be archivedto a digital library for future reference.

This chapter builds on the general design for structural engineering supportsystems described in Chapter 3 to propose a methodology by which blogs and digitallibraries can be combined into a structural engineering support system based on threeenabling modules which will be described and specified. A framework for this applica-tion is proposed and the prototype that facilitates the management and sharing of bothpersonal knowledge and computing tools is developed, with the objective of improvingthe overall efficiency and productivity of engineering design processes.1

4.2 Blog+DL Framework for Engineering Knowledge Management

Based on the SECI model, this section identifies the features of a KM systemthat promote the conversion and interaction of tacit and explicit knowledge amongindividuals. A framework for the use of Weblogs and Digital Libraries as buildingblocks for a KM system is presented.

4.2.1 Technology Application Framework

Socialization and Externalization

As depicted in the top left-hand quadrant of Figure 4.1, the first part of thespiral of knowledge creation is interpersonal. It starts when people that share the sameinterest gather to interact, socialize and externalize their tacit knowledge by sharingexperiences. A good KM system should help such people to discover each other andprovide them a convenient means for socialization and externalization.

The statistics about blogs in Section 2.3 indicate that blogs can be used as aneffective KM tool in this regard. Blog publication and the social networking featureshelp people to discover each other and create online social networks of bloggers on whichissues of common interest are expressed and personal opinions, as well as experiences,are exchanged. When Blogs is used as an engineering KM tool, an engineer may blog

1The content of this chapter has been published in Vacharasintopchai et al. (2007b).

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Figure 4.1: Blog, Digital Library, and Knowledge Conversion Modes(adapted from Nonaka and Takeuchi 1995)

about a design problem that he is trying to solve, e.g., how to setup a proper designcriterion for a construction project. The blog entry may describe the engineer’s view ofthe problem as well as the available approaches, and may also contain initial researchwork, such as hyperlinks to other blog entries about “lessons learned” from similarprojects. When published to the Internet or a corporate intranet, the blog entry canbe read by many other people, some of which may share the same interest or haveexperienced the same problem. A reader may assist the engineer in finding a solutionby posting his opinion or experience, such as the limitation of a selected approach,as a comment to the blog entry. Another reader may view the problem differentlyand may not agree with the first reader’s opinion. He may express his opinion on apersonal blog and notify the original blogger in the form of a trackback. The originalblogger can participate in the discussion thread created by this series of comments andtrackbacks and be able to find a more appropriate design criterion for his project. Thisblog discussion thread may also benefit a public audience who may have had a similarproblem and comes across this discussion thread on a Web search engine. In this way,a group of people who share the same interest is gathered; a field of interaction isconstituted; and the participants are equipped with a tool to socialize and exchangeexperiences, i.e., pieces of tacit knowledge, in blog discussion threads.

Combination and Internalization

The second part of the spiral of knowledge creation is intrapersonal. It contin-ues from the interpersonal part when a person synthesizes new explicit knowledge—combining different bodies of explicit knowledge to solve an unfamiliar problem. Once

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the new knowledge is put into practice, it becomes valuable know-how that is internal-ized into the person’s tacit knowledge base and becomes part of his skills. An effectiveKM system should help people to easily build up repositories of explicit knowledge andallow relevant pieces of knowledge in the repositories to be conveniently and preciselyretrieved so that a more complete set of knowledge is accessible and can be combinedinto the best possible new knowledge. A desirable KM system should also facilitateinternalization, i.e., help people put new knowledge into practice by, for example, pro-viding them convenient access to relevant computational software tools to shorten thetime required for a design workflow.

Keyword tagging and the selective tag browsing features of Blogs complementthe metadata rich content archiving and retrieval features of Digital Libraries andmake them suitable as effective KM tools in this regard. Blog entries created duringsocialization and externalization are typically tagged with keywords when they arepublished. In addition to conventional keyword and full text searches, the blog ownerand readers can use the selective tag browsing feature available in most blog serviceproviders to filter and browse only through relevant blog entries, allowing them tostay focused on the topics of most interest to them. An engineer can also archivethe content and metadata of mature blog discussion threads, which contain piecesof externalized tacit knowledge and reflects the group thinking processes by whichsolutions to important problems are arrived at, into the collections of the institutional-repository digital library of which he has a membership. He can also archive interestingWeb pages, such as product data sheets, and digital content, such as Excel, PowerPoint,PDF, Word, and MP3 files, into the collections. In this way, the engineer buildsup a personal portfolio of knowledge that can be shared across the organization. Adigital library, with its extensive metadata browsing and search features, as well asfull-text indexing capability, becomes a human-filtered search engine (Takeda, personalcommunication, February 26, 2007) which portfolio owners and peers can use to accessa focused set of relevant knowledge without “noise.” With all the mentioned features,Blogs and Digital Libraries can thereby enhance the efficiency and effectiveness of thecombination and internalization processes of individuals.

4.2.2 System Architecture

A system architecture that corresponds to the proposed technology applicationframework is presented in Figure 4.2. Seven components, namely, the Web Browser,the Blog Server, the Digital Library Server, the World Wide Web, the Archiver, theKeyword Suggester, and the Web Service (WS) Portfolio Manager, are involved. Thelatter three are the key components that, together with Weblog and Digital Librarytechnologies, enable the constitution of an effective knowledge management system.

The Web Browser is proposed as the primary user interface for convenient andubiquitous access to the system and also because Blogs and Digital Libraries are them-selves Web-based. An engineer can use a Web browser to create an account on a blogserver and a digital library, and use it to log on to the blog server to socialize andexternalize his knowledge by publishing blog entries or joining blog discussion threads.

The Archiver is a special-purpose component that monitors requests from aWeb browser to take a “snapshot” of a blog discussion thread or a Web page andarchive it to a specific collection in a digital library. It is the primary component thatassists in building up personal knowledge portfolios. When a blog discussion thread hasmatured, the blog owner may summarize the discussion and use a Web browser to take

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Figure 4.2: Blog+DL Framework for Computer-Aided Engineering Design

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a snapshot of the thread. The Web browser would prompt him to enter appropriatemetadata, such as the title, the author’s name, keywords, and a brief description of thethread, as well as his digital library account name and the name of the collection, i.e.,knowledge portfolio, to which he is authorized to submit. It would then submit thecollected information along with the URL of the thread to the archiver and request thata snapshot of the thread be taken and processed accordingly. In addition, an engineermay use the Web browser and the archiver to take snapshots of interesting Web pages,such as discussion threads, product datasheets, or design tips, into his portfolio. Duringthe combination and internalization processes, when explicit knowledge from varioussources is crystallized into new explicit knowledge and put into practice, the engineerthen uses the Web browser to access relevant discussion threads in blogs and pieces ofknowledge collected in personal portfolios in the digital library.

The Keyword Suggester is a second essential component in the KM systemproposed here. Since the Blog and Digital Library are independent technologies thatdo not naturally interoperate; the use of common metadata is necessary to enablesuch interoperation, i.e., the keywords assigned to blog entries and digital library itemsshould be from the same controlled set and the bibliographic information assigned tothem should conform to the same data format (e.g., DD-MM-YYYY for dates) so thatrelevant knowledge in blogs and the digital library can be retrieved simultaneously witha common search query. The Keyword Suggester is the component that ensures theenforcement of common metadata.

Besides exchanging and commenting on traditional documents and multimediacontent, an engineer can participate in the SECI modes of knowledge conversion byexchanging and commenting on computational tools, such as mathematical routines,numerical analysis modules, and design shortcuts. An engineer can develop thesecomputational tools as subroutines (or methods, in the object-oriented programmingparadigm) in his preferred programming languages, such as Java or VB.NET, and al-low them to be remotely executed by other computers as Web services on the Internet.To “publish” a Web service, i.e., to make a subroutine available for remote access, anengineer places the code of the subroutine onto a Web service server, such as ApacheAxis for Java or Microsoft Internet Information Server for VB.NET. The server au-tomatically generates a “Web service description document” that corresponds to thesubroutine. This document contains the instruction for computers and people on howto execute the service. The engineer can post the Web service description document ina blog entry, along with a brief description of the service, its theoretical explanation,and a user’s guide. In this way, the service is advertised and made accessible to thepublic. Discussions about the service, such as user experiences, can be exchanged asblog comments and trackbacks as in the ordinary socialization and externalization useof blogs. The Web services published through blog entries are discovered primarilywhen the service advertisement entries are visited by fellow bloggers. They may alsobe discovered secondarily when referred to in other discussion threads. Besides readingthe contents, e.g., descriptions and explanations, blog readers can execute the serviceor build up a portfolio of computational tools, i.e., Web services.

This availability of the portfolio of Web services necessitates the third compo-nent being suggested in the framework, namely, the Web Service Portfolio Manager.This third key component turns the proposed framework into a fully-functioning KMsystem by enabling the sharing and execution of Web services published in blog discus-sion threads. This add-on component to the blog server detects whether the content

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of a blog entry includes a hyperlink to the description document of a Web service. Ifit does so, the Web Service Portfolio Manager will provide an “execute” button and a“save” button in the Web page that displays the blog entry, so that the readers canchoose to execute the service or save it to their portfolio for later use. In the lattercase, the user will be asked to provide the description of the desired Web service whena computational service is needed. The Web service portfolio manager would searchthe user’s portfolio and retrieve a list of Web services that best match the request.Once a Web service is selected by the user, the user can execute the Web service byentering the input data and clicking the execute button. The selection of Web servicesfrom portfolios and the execution of Web services are performed intelligently accordingto the Semantic Web Services Framework for Computational Mechanics (SWSCM),which is proposed and explained in the next chapter. The Web service portfolio man-ager performs the consumer and the matchmaker roles in the SWSCM. Readers arereferred to the mentioned reference for in-depth information about the Semantic WebServices technology and its application in engineering design.

4.3 A Semantic Computing-Based Support System for Structural Engi-

neers

4.3.1 Implementation

Based on the proposed Blog+DL framework for computer-aided engineeringdesign, a prototype system has been developed to assist engineers in socializing andexternalizing their knowledge, as well as in building up personal portfolios of knowl-edge and computational tools. The knowledge and tools in such portfolios can becombined and crystallized into new knowledge and can also be shared with colleaguesof the engineers to form a larger organizational KM system. The prototype systemcontains structural design aids, such as properties of steel sections from various in-dustrial standards, as well as Web services and worksheets for structural analyses anddesigns. It also includes articles and discussion threads about general sciences andapplications of information and communication technologies in structural engineering.The implementation of the prototype system follows the system architecture presentedpreviously which consists of six components, namely the Web Browser, the Blog server,the Digital Library server, and the three KM enabling components: the Archiver, theKeyword Suggester, and the Web Service (WS) Portfolio Manager. A federated searchagent was additionally introduced for even improved productivity.

The prototype system was implemented in Java and JavaScript and utilizedplatform neutral open-source software tools. The server components were deployed ona Windows XP Professional Web server at http://kb.blogdns.org:8080/blojsom.

Web Browser Mozilla Firefox (Mozilla, 2007), a standards conformant Web browseravailable on major computer platforms, such as Windows, Linux and Mac OS X,was chosen as the Web Browser component. Firefox is highly extensible and fullysupports bookmarklets—JavaScript codes embedded in Web browser bookmarks—which are useful as custom user interface components.

Blog Server Blojsom (Czarnecki, 2007), a blog server developed in Java with ex-tensive support for third-party add-on modules, was chosen as the Blog server.Optional features useful for KM, such as site-wide article aggregation, selectivetag browsing and keyword tagging, as well as full-text searches, were installed

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and customized.

Digital Library Server DSpace (DSpace, 2007a), a widely accepted large-scale Javadigital library server based on the institutional repository concept, with an exten-sive documented set of application programming interfaces (APIs), was chosenas the Digital Library server. Several collections, such as general “StructuralEngineering,” “Steel Design,” “Reference,” “News,” and “Tips and Techniques”were created to store user contributed contents.

Archiver The Archiver was developed as a Java servlet that monitors requests toarchive documents at particular URLs along with the provided sets of metadatainto specific DSpace collections. An archive bookmarklet was developed as abridging user interface between Mozilla Firefox and the Archiver (Figures 4.7a, b).The bookmarklet was deployed as a Save to DSpace button in the Mozilla Firefoxbookmarks toolbar. Once clicked it prompts the user to enter a brief descrip-tion and the metadata of a Web page and forwards the request to the Archiver.The Archiver utilizes DSpace APIs, such as the methods of classes Item andCollection in the org.dspace.content package, and the JavaMail APIs (SunMicrosystems, Inc., 2007) to create and store standard compliant MHTML doc-ument archives (Palme et al., 1999) into the DSpace repository. By adopting theMHTML Internet standard, document archives generated by the Archiver can bereadily processed by the built-in full-text indexer of DSpace and are displayableon major Web browsers, such as Microsoft Internet Explorer, Mozilla Firefox andOpera.

Keyword Suggester The Keyword Suggester was implemented as a servlet that pro-vides ontology-assisted keyword suggestions. It takes input strings from HTTPPOST requests and consults a predefined set of ontologies to extract relevant key-words from Web contents at specific URLs by using APIs from Jena (Jena, 2007),an open-source Semantic Web programming toolkit originated from Hewlett-Packard Laboratories, and Apache Lucene (Apache, 2007), a high-performancetext search engine toolkit from Apache Software Foundation. The bookmarkletuses Asynchronous JavaScript and XML (AJAX) programming techniques toconsult the Keyword Suggester and retrieve a list of suggested keywords for theWeb page being saved. JavaScript codes in the bookmarklet prepopulate thesekeywords into the keywords textbox to assist users in entering proper metadata.Ontology-assisted keyword suggestion is helpful to enforce a controlled set of key-words, which is necessary for the interoperation of Blog and Digital Library andthe effective retrieval of content in large digital library collections. The same func-tionality is also provided in the blog entry editing module of Blojsom to suggestproper keyword tags when entries are created or modified (Figure 4.4b). Read-ers are referred to Jakobsen (2006) for an in-depth explanation on bookmarkletprogramming.

Federated Search Agent A federated search function which allows users to locatepieces of knowledge across blog entries and digital library collections is providedfor increased productivity. Like the Archiver, the federated search agent wasdeveloped as a Java servlet that monitors requests to search for keywords ormetadata. A search bookmarklet was developed as a bridging user interfacebetween Mozilla Firefox and the Federated Search agent (Figure 4.8c). The

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bookmarklet was deployed as a Search button in the Mozilla Firefox bookmarkstoolbar. Once clicked it prompts users to enter search keywords or metadataand forwards requests to the Federated Search agent. The agent utilizes APIssuch as methods of classes SimpleSearchServlet and AdvancedSearchServlet

in the org.dspace.app.webui.servlet package of DSpace, methods of classBlojsomFetcher in the org.blojsom.fetcher package of Blojsom and full-textsearch functions of Apache Lucene, the underlying search engine used by Blojsomand DSpace, to locate relevant pieces of knowledge and consolidate them into asingle Web page for convenient access by users (Figure 4.8d).

Web Service Portfolio Manager The Web service portfolio manager consists oftwo important subcomponents: the Blojsom plug-in and the Web service port-folio management servlet. The Blojsom plug-in implements the BlojsomPlugin

interface class in the org.blojsom.plugin package of Blojsom to preprocess thecontents of blog entries before they are displayed on Web browsers. By usingthe regular expression string matching technique, the plug-in detects whetherblog entries contain links to Web service descriptions, such as WSDL or OWL-Sfiles. If they do so, (1) Save buttons are inserted into the HTML codes of blogentries such that, once clicked, users’ log-in names and URLs of Web service de-scriptions are forwarded to the Web service portfolio management servlet, whichmaintains the registration of Web services in users’ portfolios; and (2) user inter-face components such as input boxes and submit buttons are inserted into theHTML codes of blog entries for immediate execution of Web services, with theassistance of the OWL-S API (Sirin, 2004), WSDL4J (WSDL4J, 2006) toolkitand Apache Web Service Invocation Framework (WSIF) (Apache, 2003b). TheWeb service portfolio management servlet handles the registration, unregistrationand selection of services in users’ portfolios. It was developed as a Java servletthat monitors requests to add, update or delete services in particular portfolios,identified by log-in names. It also acts as a proxy to the Matchmaker servicedescribed in Section 5.3 on page 51, which implements the matchmaking algo-rithm in Section 5.2 to help users select appropriate services from many they havecollected. Java hash tables and property files were used as building blocks forservice registration databases. OWL-S API and WSDL4J toolkits were used toextract information from Web service descriptions. WSIF was used for the execu-tion of Web services. Jena Semantic Web toolkit and RACER Description Logicreasoner (Haarslev and Moller, 2003) were used to provide intelligence assistanceto the matchmaker. Intelligent interoperability of Web services are explained indetail in the next chapter.

4.3.2 A Proof of Concept Prototype

Figures 4.3 through 4.8 illustrate a typical application of the prototype, a walk-through example of which is demonstrated as follows:

Installation

Initially, an engineer needs to create an account on the system by visiting thehomepage of the Blog and the Digital Library server. An instruction on the homepagewill direct him to the services page where his user name is verified (Figure 4.3a), URL topersonal blog assigned, membership of DSpace collections granted, and Save to DSpace

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(a) Account Verification (b) Personalized Bookmarklets

Figure 4.3: User-specific Initialization of Prototype

and federated Search bookmarklets personalized (Figure 4.3b). Bookmarklets must bedragged and dropped onto the bookmark toolbar of Mozilla Firefox before they can beused.

Socialization and Externalization

In terms of publication, to externalize his knowledge and socialize, the engineerlogs on to his blog and clicks Manage to enter Blojsom administration console. Tocreate a new blog entry, he chooses the Add Entry command in the Entry toolbar whichdirects him to the Add Blog Entry page. Here, he can start blogging about new findings,as well as ideas and plans related to a project, in a fashion similar to composing e-mails (Figure 4.4a). The content of his entry is automatically analyzed for importantkeywords as he blogs, thanks to embedded AJAX codes, preregistered ontologies andthe Keyword Suggester. Checkboxes of suggested keywords are presented in real timein the Smart Keyword Suggestions section where he can pick ones that apply. He mayalso add invented keywords into the User’s specified textbox, although such a practiceis discouraged for vocabulary control reasons (Figure 4.4b). The composition of blogentry completes when the Add Blog Entry button is clicked. New entries are saved intothe server’s database.

Entries by individual users are listed in a reverse chronological order on their per-sonal blogs, accessible at the URLs http://http://kb.blogdns.org:8080/blojsom/blog/[user_name]. Tag clouds, lists of tag names with varying font sizes, which rep-resent different frequencies of keywords used by particular users, are also provided sothat visitors can easily navigate through various aspects of the authors’ interests (Fig-ure 4.4c). Recent entries by all users are additionally aggregated on the prototype’shomepage at the URL http://http://kb.blogdns.org:8080/blojsom (Figure 4.4d)for quick overview of new contributions from all users.

In terms of consumption, the engineer can browse through blog entries by visit-ing the prototype’s homepage as well as personal ones of acquainted colleagues. He maycheck for updates by paying regular visits to respective sites. Such a practice is consid-ered the “pull” method of blog consumption. He may also choose the “push” methodto subscribe to blogs, as well as aggregated entries on the homepage, by clicking at theorange Web feed icon at the right end of Web browser address bars, cf. Figures 4.4c, d.By doing so, he is next presented with a Web feed subscription screen (Figure 4.5a)where he can allow updated contents to be regularly pushed to him via a blog reader.Figure 4.5b illustrates how a personal blog is subscribed as a Web feed source in Mi-crosoft Outlook 2007. Excerpts from recent entries on the blog are regularly pushed

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into the engineer’s Inbox, along with other e-mail items (Figure 4.5c). Interesting blogentries can be opened and read in a fashion similar to that for e-mails (Figure 4.5d).The engineer can click the View article button to visit original blog entry pages to readfull articles and participate in discussion threads with his peers (Figure 4.6a).

Combination and Internalization

During day-to-day tasks, engineers often come across new knowledge that aresometimes not related to present tasks but may be useful in the future. The engineercan log on to his DSpace portfolios (collections) and collect pieces of knowledge, aswell as their metadata, into the knowledge repository. For improved productivity, Webcontents such as blog discussion threads, datasheets and design aids can be directlyarchived into DSpace collections by clicking at the Save to DSpace button on MozillaFirefox’s bookmark toolbar. The engineer will be prompted to enter bibliographicmetadata, i.e., title, authors, keywords and brief descriptions, and to select a DSpacecollection most appropriate for the new knowledge piece (Figures 4.7a, b). Non-Webcontents such as printed documents can be digitized into electronic forms, such as PDFand JPEG files, and are archived into the repository through DSpace’s standard Webinterface (Figure 4.7c). Figure 4.7d illustrates a personal knowledge portfolio builtupon the accumulation of knowledge pieces into DSpace collections.

Knowledge shared in blog entries as well as those accumulated in personal port-folios are invaluable resources to promptly solve the engineer’s problems at hand. Hecan also use these knowledge to help others by providing suggestions and URLs toknowledge pieces in colleagues’ discussion threads. When working on a project simi-lar to some in the past, archived documents in the repository can be readily accessedthanks to DSpace’s metadata and full-text search facilities (Figure 4.8a). If he feelshe had seen design tips related to this type of projects in a colleague’s Web feed, hecan use Blojsom’s keyword and full-text search features to locate that particular blogentry (Figure 4.8b). For improved convenience, the engineer can also use the fed-erated Search bookmarklet to simultaneously search through discussion threads andknowledge portfolios contributed by him and colleagues (Figure 4.8c). Search resultsare summarized in single Web pages where articles are readily accessed by one mouseclicks (Figure 4.8d). Web services published through blog entries can also be executedright away from results pages with the assistance of the Web Service Portfolio Managerdescribed earlier (Figure 4.8e).

In this way, the engineer and fellow staff members are equipped with an inte-grated set of tools to better share knowledge and experience.

4.4 Summary

This chapter has proposed a framework for the application of Weblog, DigitalLibrary, and Semantic Web Services technologies to support structural engineers dur-ing the engineering process. The framework is based on the theory of organizationalknowledge management which postulates that knowledge is created by the interactionsbetween tacit knowledge and explicit knowledge among individuals in an organization.In contrast to the KM initiatives in the late 1990s, the focus of this framework is onfacilitating the conversion, interaction, and sharing of tacit and explicit knowledge,which are the bases for creating knowledge, rather than on capturing the individualpieces of explicit knowledge. The proposed framework also differs from many modernKM tools for engineering design firms in the literature because it is based on several

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Internet standards beyond HTTP and HTML and therefore promotes cross-system in-teroperability and advanced applications of such Web technologies as Web feeds andWeb Services, which are enabling technologies for Web 2.0 (a.k.a. the social Web) andInternet-based multiplatform distributed computing. The introduction of SemanticWeb Services technologies and the application of ontology, as a fundamental compo-nent of the Semantic Web, further open KM systems that conform to the proposedframework to a wide variety of Internet-based artificial intelligence technologies. Theproposed framework can improve the knowledge and productivity of engineers in to-day’s ever more globalized and competitive world.

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(a) Creating New Blog Entry

(b) Ontology-assisted Keyword Suggestion

Figure 4.4: Blog Publication Features of Prototype

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(c) Tag Cloud (on right)

(d) Recent Blog Entries

Figure 4.4: Blog Publication Features of Prototype (cont’d)

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(a) Subscribing to Web Feed

(b) Subscribed Web Feeds in Microsoft Outlook

Figure 4.5: Blog Consumption Features of Prototype

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(c) Using Microsoft Outlook as Web Feed Reader

(d) Reading Blog Entries by Microsoft Outlook

Figure 4.5: Blog Consumption Features of Prototype (cont’d)

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(a) Discussion among Colleagues

Figure 4.6: Blog Socialization Features of Prototype

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(a) Archiving Discussion Thread to DSpace

(b) Archiving Webpage to DSpace

Figure 4.7: Knowledge Archiving Features of Prototype

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(c) Archiving Non-Web Content

Figure 4.7: Knowledge Archiving Features of Prototype (cont’d)

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(d) Personal Portfolio of Knowledge

Figure 4.7: Knowledge Archiving Features of Prototype (cont’d)

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(a) Digital Library Search

(b) Blog Search

Figure 4.8: Knowledge Retrieval Features of Prototype

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(c) Federated Search

(d) Search Results

Figure 4.8: Knowledge Retrieval Features of Prototype (cont’d)

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(e) Web Service Execution

Figure 4.8: Knowledge Retrieval Features of Prototype (cont’d)

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CHAPTER 5

ENGINEERING SOFTWARE INTEROPERABILITY

5.1 Introduction

Computers performing structural analysis using numerical methods require closeguidance from human users. Consider the case of finite element analysis, in which it isfirst necessary to examine a real world problem and construct a computer model of theproblem including the problem domain, material behaviors, and boundary conditions.It may also be necessary to consult many design codes or previously obtained experi-mental results to assemble the proper input data for the analysis. Next, the problemdomain must be discretized, either manually or through the help of automatic meshingsoftware, before the element stiffness matrices can be computed and assembled, theboundary conditions applied and a solution for nodal unknowns found. Experienceand judgment are required by the analyst to select the element types and the consti-tutive models for the materials and physical processes being simulated, to examine theaccuracy of the analysis results and, if necessary, to repeat the entire process to studysolution convergence. For large and complex analyses, running an entire process maytake hours or days and visualization techniques, including animations or virtual real-ity, may be needed to effectively interpret the analysis results. Such situations may beeven more complicated when incompatibilities between input and output data formatsof the software components employed exist, and are likely to be tedious and lead toinaccurate solutions, especially if performed by inexperienced users.

Web Services (W3C, 2004d) and the Semantic Web (Berners-Lee et al., 2001),two Internet technologies, have the potential to help unify and utilize scattered re-sources, such as personal computers, computer clusters, supercomputers, databasesand knowledge bases to enable numerical structural or mechanical analyses to be donein a fast, accurate and automated manner. These technologies potentially reduce theneed for analysts to be actively involved in providing all the required data and detailedinstructions. Rather, analysts can initiate the solution process by giving simple in-structions, monitoring the computers as they work on obtaining solutions, and makinga final decision on the acceptability of the results. With today’s ubiquitous access, theInternet is not simply a hyper-library for information searches, nor only a communica-tion tool for sending e-mail messages. It may, in fact, be utilized as a large platformfor scientific computation with the help of intelligent software agents that collaborateto accomplish a given task.

A framework for the joint application of Web Services, the Semantic Web andSemantic Web Services to address software interoperability problems in structural en-gineering is proposed in this chapter, a proof of concept prototype presented and itspotential illustrated by a case study which involves the solution of a typical problemin structural analysis and design.1

5.2 A Semantic Web Services Framework for Computational Mechanics

As discussed earlier, Semantic Web Services have the potential to help unifycomputing resources and knowledge scattered on the Internet into a large platform forscientific computing. In this section, an architectural framework is proposed, from asystem development point of view, for the development of computational mechanics

1The content of this chapter has been published in Vacharasintopchai et al. (2007a).

44

MatchmakerAPP LOGIC,

KB, DB

Service ConsumerAPP LOGIC,

KB, DB

Service ProviderAPP LOGIC,

KB, DBFi

ndPublish

Bind

Service Description

SOAP (XML+RDF)

OWL-S

Figure 5.1: Semantic Web Services Model

software based on Semantic Web Services. To prevent ambiguity with terms in thegeneral Semantic Web Services architecture, the proposed framework is called SemanticWeb Services for Computational Mechanics (SWSCM), and a semantic Web service inthe SWSCM architectural framework is called an SWSCM Service.

5.2.1 SWSCM Model

The proposed SWSCM is an extended version of the standard Web Servicesmodel presented in Figure 2.1 on page 10. The model of the SWSCM architecture isillustrated in Figure 5.1. The goal in the development of the SWSCM framework is tomake it as compatible with the prevalent Web Services technology as possible, i.e., wetry to make SWSCM the semantic “shell” of Web Services. By adopting such a shellprinciple, we will have access not only to a variety of software development tools forWeb Services technology, which are available in several programming languages, butalso the many Web services deployed on the Internet.

To facilitate the publication and discovery of relevant semantic Web services, theUDDI service registry is replaced by the matchmaker service, and the WSDL servicedescription is replaced by the OWL-S semantic Web service description (Figures 2.1and 5.1). Access to WSDL documents on the Internet is still necessary for serviceconsumers to properly ground and bind to service providers. However, the matchmakerdoes not need to store the copies (or URL to the copies) of such documents locallybecause only the semantic Web services description from OWL-S documents is used inthe matching process.

SOAP messages, which are used for communication between Web services, areaugmented with RDF statements so that XML data transmitted from a sender willbe meaningful to the recipient. Each parameter of the request and response mes-sages between service consumers and providers consists of the serialized XML data(as required by the SOAP specification) and its corresponding RDF statements whichexplain the meaning of such data, as shown in Figure 5.2. RDF statements are typi-cally encoded in XML format and the format is termed RDF/XML according to theRDF specification. As RDF/XML can naturally be included as extra attachments inSOAP messages, RDF-augmented SOAP messages will not cause a compatibility issue

45

SOAP Envelope

SOAP Body

REQUEST / RESPONSEMESSAGE

XMLDATA

Parameter 1

RDF

...CO

NS

UM

ER

PR

OV

IDE

R

XMLDATA

Parameter n

RDF

Figure 5.2: Semantic SOAP Message

DATABASE

Que

ry

Pro

cess

or Table A

Table B

Table n

...

APP LOGIC

Objects - fields - methodsInstances

KNOWLEDGE BASE

Infe

renc

e E

ngin

e

Concepts

Assertions

Figure 5.3: Components of Semantic Web Service

with non-SWSCM aware recipients because the recipients can ignore the part of anattachment that they do not recognize.

5.2.2 Components of SWSCM Service

Each SWSCM service comprises three components, which are the applicationlogic component, the knowledge base component, and the optional database component,as illustrated in Figure 5.3.

The application logic is the primary component of a SWSCM service. It per-forms the functions advertised in its service description. It handles and processesrequests initiated by service consumers, and delegates tasks to the others by sendingout request messages. The application logic is the same component found in typicalservices on the Web Services architecture.

The knowledge base provides intelligent assistance to the application logic. Itconsists of an inference engine and copies of ontologies and RDF (or OWL) state-ments downloaded from the Semantic Web. Multiple domain ontologies, e.g., a units-of-measurement ontology, a material property ontology, and a structural engineeringontology, are downloaded and stored locally on the knowledge base during the initial-ization process of an SWSCM service. The knowledge base uses its inference engineand the premises from RDF (or OWL) statements to answer queries initiated from theapplication logic.

The database component provides information processing assistance to the ap-plication logic. It is an optional component which contains a dedicated database man-agement system. The database component is useful when an SWSCM service has todeal with a large amount of raw data and complicated database queries, as in the

46

case of postprocessing the results from a large-scale finite-element analysis which caninvolve several million degrees of freedom.

5.2.3 Communication between SWSCM Services

In standard Web Services architecture, to send a request to a service provider,a service consumer first serializes its respective input parameters—“objects” in theobject-oriented programming paradigm or “structures” in the procedural programmingparadigm—into a structured XML representation (shown as XML data in Figure 5.2).According to the WSDL standard by W3C, the structure of the XML representation(termed XML schema) is agreed upon in the “Types” section of the WSDL documentpublished by the service provider. Next, the service consumer consecutively wrapsthe input parameters with the SOAP Body and SOAP Envelope XML elements, andfinally uses the HyperText Transfer Protocol (HTTP) to transmit the SOAP envelopeto the service provider at the “rendezvous URL,” which is also specified in the WSDLdocument. Once the service provider receives the SOAP message from the consumer,it does the reverse procedure to extract the input parameters and deserialize them intoobjects or structures, depending on its programming paradigm. Later, it processes theinput data, serializes the output into XML format, constructs the SOAP message, andfinally transmits the result back to the service consumer at the rendezvous.

SWSCM services communicate in the same fashion as standard Web services do,i.e., through SOAP messages. The WSDL grounding section of an OWL-S descriptiongrounds the generic input parameters, output parameters, and atomic processes tospecific sections of a WSDL document that specify the XML schema of the inputparameters and output parameters, and the rendezvous URL for actual invocation ofthe Web service. At the syntactic level, SWSCM services refer to the XML schemaand rendezvous URL for the serialization of objects or structures and the transmissionof SOAP envelopes, respectively.

In addition, at the semantic level, the communication between SWSCM ser-vices extends the standard SOAP messaging protocol by embedding the RDF state-ments relevant to the objects or structures carried by SOAP messages (Figure 5.2).It is proposed that the application logic component of each SWSCM service assignseach object or structure a unique URI at runtime and that it populates its associatedknowledge base component with a set of assertions about the object or structure. Forexample, a floating-point object Width 01 with the value of 2.00, which representsthe inch width of a rectangle, could be given the URI of http://ws.example.com/object_ids#Width_01. If we refer to this URI as URI W01, its corresponding set ofassertions would be the RDF statements:

URI W01 “is an instance of” http://std.swscm.org/geometry.owl#Width; and

URI W01 “has unit” http://std.swscm.org/units.owl#Inch.

A pair of an object (or a structure) in the application logic and its assertions in theknowledge base is termed a Semantic Object .

During the serialization process, when an SWSCM service consumer sends arequest message to the provider, the application logic component not only serializes aninput parameter (an object or a structure) into the XML Data, but also provides anaccompanying set of RDF/XML assertions (shown as the RDF block in Figure 5.2).Having received and deserialized the SOAP message, the SWSCM service provider willbe able to not only reconstruct the data set of input parameters but also replicate the

47

assertions about each input parameter into its own knowledge base. In addition tothe semantic matching of Web service description profiles, which is discussed in thesection on “Semantic Web Services”, the proposed RDF-augmented SOAP messagesprovides an extra cross-checking means to address the incompatibility issue between theinput and output parameters. RDF-augmented SOAP messages would help preventthe kind of problem that causes mishandlings of physical quantities in the mission-critical calculations, which occurs, for example, just because one service expects theinput parameters in metric units of measurement and another service provides theinput parameters in English units, and the two services by chance refer to their inputparameters by the same ontological concept. The RDF augmented SOAP messagingin the SWSCM framework is termed the Semantic SOAP .

5.2.4 Inference Engine and Reasoning Process

The proposed SWSCM framework is independent of an inference engine. Anyinference engine can be used provided that they are equipped with the basic rulesfor processing RDF and OWL statements. As discussed in the sections on “SemanticWeb” and “Semantic Web Services”, reasoning on RDF and OWL statements reliesheavily on concept subsumption, which is the determination of whether one concept ismore general than another. For instance, term X subsumes term Y if term X is moregeneral than term Y, and term X is the same as term Y if term X and term Y subsumeeach other. Therefore, the inference engine for the knowledge base component of anSWSCM service must satisfy the following requirements:

1. It must be capable of parsing the statements encoded in RDF and OWL lan-guages;

2. It must natively understand or be equipped with the set of rules to understandthe semantics of RDF and OWL constructs as specified by W3C (W3C, 2004c,b);and

3. It must at least be able to answer concept subsumption problems.

An inference engine that understands the semantics of RDF and OWL will be able toanswer queries similar to the following examples, with terms in the quotation marksreferring to standard constructs in the RDF and OWL specifications.

Example 1

Given the premise RDF statements:

term X is an “instance of” class X;

class X is a “subclass of” class Y; and

class Y is a “subclass of” class Z,

then

Query : Does class Z subsume class X? Answer : Yes, by transitivity.

Query : Is term X an “instance of” class Z? Answer : Yes, because term X is aninstance of class X which is subsumed by class Z.

48

Example 2

Given the premise RDF statements:

Triangle is a “subclass of” PlaneShape;

Triangle is a “subclass of” ClosedShape;

Triangle “has side” of Line only; and

The “number of” “has side” properties that Triangle has is 3,

then, if an object is defined by the following assertions:

The object is a “subclass of” PlaneShape;

The object is a “subclass of” ClosedShape;

The object is a “subclass of” EquilateralShape;

The object “has side” of Line A, which is an “instance of” Line;

The object “has side” of Line B, which is an “instance of” Line;

The object “has side” of Line C, which is an “instance of” Line; and

The “number of” “has side” properties that this object has is 3.

Query : Is the object a Triangle? Answer : Yes, because based on the givenpremises, Triangle subsumes the definition of the object; therefore, the object is akind of Triangle.

Examples of inference engines that can be used in SWSCM knowledge base compo-nents are RACER (Haarslev and Moller, 2003) and the SWI-Prolog Semantic Webpackage (Wielemaker et al., 2003), which are based on Description Logics (Nardi et al.,2003) and Prolog, respectively. RACER is an example of an inference engine that na-tively understands the semantics of RDF and OWL constructs because its underlyingDescription Logics framework was used as the basis to design the OWL language. SWI-Prolog with its Semantic Web package is an example of an inference engine equippedwith a set of rules that enables it to understand the semantics of the RDF and OWLconstructs.

It should be noted that RDF and OWL are knowledge “representation” lan-guages. The RDF and OWL specifications do not mandate the use of DescriptionLogics as the only logical framework for the Semantic Web. The knowledge inferred byan inference engine, or the answer that it provides, is limited by its underlying logicalframework.

49

5.2.5 Matchmaking Algorithm

One of the most important operations in the Semantic Web Services architectureis the matching of the ideal service profile of a service consumer against the service pro-files registered by several service providers. In this section, the service profile matchingalgorithm proposed for use by matchmakers in the SWSCM architecture is discussed.The algorithm is inspired by the one proposed by Semantic Web Services researchersat Carnegie Mellon University (Paolucci et al., 2002; Sycara et al., 2003).

An OWL-S profile description is a set of OWL-S statements that semanticallydescribes a service, which is either needed by a service consumer or offered from a serviceprovider. From the section on “Service Profiles” in the OWL-S specification (OWL–S,2003), the elements of a profile description that are relevant to the interoperation ofSWSCM services are the taxonomic type of profile (i.e., whether a service belongsto a certain class) and the “has input,” “has effect,” and “has output” properties.Examples of such elements are the <profileHierarchy:SineFunctionEvaluator>,the <profile:hasInput>, the <profile:hasEffect>, and the <profile:hasOutput>in Figure 2.4 on page 17, respectively.

For each pair of the service profiles, the degree of match is calculated by usingthe weighted average of the matching scores between the pairs of the profile types, theinput parameters, the effects of service, and the output parameters. Mathematically,the degree of match between a pair of service profiles is

DS =

∑

i widip

∑

i wi

(5.1)

where DS = degree of match between two service profiles; and wi and dip = respective

weight and the matching scores between the profile types, the input parameters, theeffects of services, and the output parameters. By default, equal weights are assigned tothe matching scores in typical matchmaking operations. Service consumers may requesthigher weights to certain pairs of the profile description if compatibility between thosepairs is more important.

For each pair of the ideal concept CR and the advertised concept CA, the match-ing score between CR and CA with respect to CR, d(CR, CA), is defined as

d(CR, CA) =

1.00 if CR is the same as CA,

0.75 if CA subsumes CR,

0.50 if CR subsumes CA,

0 otherwise.

(5.2)

If we collectively call the profile types, the input parameters, the effects of service, andthe output parameters “parameters,” the value of d(CR, CA) = 1.00 signifies that theideal parameter perfectly matches the advertised parameter. The value of 0.75 signifiesthat the advertised parameter is more general than the ideal parameter, and that theadvertised service is not specifically made for the consumer. The value of 0.50 signifiesthat the ideal parameter is more general than the advertised parameter, and that theadvertised service may not completely fulfill the consumer’s request. The value of 0signifies that the two parameters are incompatible and the advertised service is notrecommended for the consumer (Paolucci et al., 2002).

SWSCM services often take several input parameters. In this case the matchingscore between the two sets of input parameters is the unweighted average of the match-

50

ing scores between each sequential pair of the input parameters. Mathematically, it isdefined as

dIp =

∑

j djI

N(5.3)

where dIp = matching score between two sets of input parameters; dj

I = degree ofmatch between the jth pair of input parameters; and N is the total number of inputparameters. To eliminate the case of strong incompatibilities between certain pairs ofinput parameters, dI

p must be taken as zero if any of djI equals zero.

5.3 Application to Structural Analysis and Design

5.3.1 Implementation

A prototype system was developed to determine the maximum stress in a rect-angular solid object of various configurations. The object, made of a material selectedby the user, was to be loaded uniformly under a user-given load specification andsubjected to user-given support conditions. The dimensions of the object were to bespecified in different units of measurement. The specific objectives in developing theprototype were:

1. to demonstrate the interoperability of heterogeneous software components, asoffered by ontologies, OWL statements, semantic Web service descriptions, andthe matchmaking concepts; and

2. to demonstrate the role of OWL statements, published on the Semantic Web, asdynamic and external data sources for structural analysis software components.

The following open source and public-domain software tools were used in theimplementation:

1. Apache Axis Web Services toolkit (Apache, 2003a),

2. Apache Web Service Invocation Framework (WSIF) (Apache, 2003b),

3. JAMA matrix library (NIST, 1999),

4. Jena Semantic Web Framework (Jena, 2007),

5. University of Maryland’s OWL-S API project (OWL-S API) (Sirin, 2004),

6. Protege ontology development tool (Protege, 2005), and

7. RACER Description Logic reasoner (Haarslev and Moller, 2003).

All Java Web services utilized the APIs provided by Apache Axis and WSIFto initiate and process SOAP requests and responses during the execution of Webservices. Custom serializer and deserializer modules were developed in Java to allowApache Axis to recognize and process Semantic SOAP messages. Matrix operationsin Java Web services employed the APIs by JAMA matrix library. Java softwaredevelopment kit version 1.4.1 and Microsoft Visual Studio .NET 2003 were used asthe compilers for Java and C# codes. All SWSCM services employed the inference

51

Structural Analysis WS

ParEFG WS

FEM WS

Mesher WS

Max Value WS

USER-INTERFACE(PDA/Smartphone)

Matchmaker

Figure 5.4: System Configuration of Prototype

engine and RDF knowledge base query and management APIs provided by Jena. TheMatchmaker employed the inference engine and RDF knowledge base APIs providedby RACER, a dedicated Description Logic reasoner, for more powerful reasoning oncomplicated ontologies. The Matchmaker utilized OWL-S API to extract conceptsrepresented in OWL-S service profiles. SWSCM services used OWL-S API to extractservice grounding information for binding to service providers. Grounded services wereexecuted by means of the APIs in WSIF. The Protege ontology development tool byStanford University was used in the development of ontologies.

The prototype system consisted of five SWSCM services, one Matchmaker ser-vice and one User Interface, as shown in Figure 5.4. The SWSCM services consistof the Structural Analysis (SA) service, the Mesher service, the Parallel Element-FreeGarlerkin Method (ParEFG) service, the Finite-Element Method (FEM) service andthe Max Value service. WSDL documents and OWL-S documents, as well as ontologiesand OWL statements, were published on a Web server to facilitate the operation ofSWSCM services and the Matchmaker. WSDL documents describe the syntactic oper-ations of SWSCM services, e.g., the schemas of XML Data and the rendezvous URLswhere SOAP messages are expected. OWL-S counterparts provide semantic profiledescriptions with grounding references to specific parts of the WSDL documents. Idealservice profiles desired by SWSCM consumers were published as nongrounded OWL-Sdocuments. Ontologies developed and published on the Web server include:

1. the Quantities ontology that defines physical quantities and various types of num-bers;

2. the Material ontology that defines materials and their mechanical properties;

3. the Units ontology that defines relations between units of measurement (e.g., “anangular degree is a subclass of plane angle, has conversion factor of π/180 withradian angle as the reference unit of measurement”); and

4. the Structural Engineering ontology that defines general knowledge in structuralengineering (e.g. the pinned support condition).

52

The prototype system was deliberately designed to consist of modules devel-oped on potentially incompatible platforms. As postulated in Section 5.2.1, OWL-S,WSDL and SOAP are the common languages that SWSCM services “speak.” Ser-vices on different computing platforms were developed in appropriate programminglanguages such that they speak the same dialects and communicate by the methodol-ogy in Section 5.2.3. Key characteristics of modules in the prototype are presented asfollows:

Java Web Services The Matchmaker, the SA service, and the Max Value servicewere developed as Web services in Java. The Matchmaker employed the serviceprofile matching algorithm in Section 5.2.5 to handle requests for service recom-mendation. The SA service assisted the User Interface in numerical analyses ofstructures. The Max Value service employed the Quicksort algorithm to identifymaximum values from lists of double-precision numbers.

Commercial Software Package The Mesher and the FEM services were developedin Java as Web service frontends to the ANSYS commercial FEM software (AN-SYS, 2006). The Mesher, upon receiving Semantic SOAP request messages, de-serializes input parameters then generates and executes ANSYS input files whichdiscretizes problem domains into eight-noded brick meshes. The FEM service,in a similar manner, takes Semantic SOAP request messages then generates andexecutes ANSYS input files to solve particular problems correspondingly. Resultsfrom these services are serialized and returned to service consumers at respectiverendezvous URLs.

Parallel Cluster Computing The ParEFG service, programmed in Java, allowedother SWSCM services to access the Element-Free Garlerkin Method (EFGM)meshless analysis engine developed in C and deployed on a Linux parallel com-puter cluster (Barry and Vacharasintopchai, 2001). It acts as a proxy for SWSCMservices to access the functionality of the EFGM engine which per se takes inputs,receives commands and delivers outputs through UNIX socket communication(Metcalfe and Gierth, 1998).

Mobile Computing The User Interface was developed for Windows Mobile personaldigital assistants (PDAs) or smartphones, using the C# programming languageand Microsoft .NET Web Services client APIs (Microsoft, 2003). It relies on theSA service which acts as a proxy, allowing devices with lower computing powerto access the more powerful computing power provided by ParEFG, FEM andMax Value services.

Ubiquitous Access The User-Interface was designed to communicate with the SAservice via the General Packet Radio Service (GPRS) on mobile phones (Wikipedia,2006a) or Wi-Fi Internet connections (Wikipedia, 2006b). In this regard, the sys-tem prototype illustrates not only the interoperability between .NET and JavaWeb services but also the ubiquitous access to computational facilities made pos-sible by Web Services technology.

5.3.2 A Proof of Concept Prototype

The following describes an example workflow of components in the system pro-totype to solve a particular problem of finding “the maximum service stresses in an

53

Figure 5.5: Windows Mobile User Interface

ASTM A36 steel plate with the dimensions of 1.00–m wide by 200–cm long with0.25–in thickness, cantilever-supported along the longer edge, and subjected to thefloor load for libraries specified in the Thai Building Code.”

The workflow started when the user entered the input data into the User In-terface shown in Figure 5.5. From the left of the figure, the geometry of the problemwas entered at the Geometry screen, and the “Generic ASTM A36 Steel” material,the “Library Floor” load condition, the “TH-2527” building code, the “Service Load”load factor, and the “Longer-edge Cantilever” support condition were selected at theParameter screen. The Summary screen allowed the user to verify the input data, andto submit the request to the SA service. Except for the numerical values, the inputparameters were selected through the “Select a Resource” screen which retrieved pa-rameter choices from an external XML data file. The preferred unit of stress can beselected on the configuration menu and in this example the unit of stress was set topascals (Pa). The content of the SOAP body submitted by the User Interface is shownin Figure 5.6. The width, length, and thickness of the object are accompanied by re-spective URIs to the units of measurement. The material type, the support condition,the building code, the loading condition, and the load factors were specified as URIsof concepts. The preferred unit of stress was specified as the URI to the concept “Pa.”

The input parameters were deserialized by the SA service into semantic objectsas soon as the request message arrived. Preconfigured to work in the SI units, e.g.,lengths in meters and stresses in pascals, the application logic of the SA service queriedthe knowledge base for conversion factors, and the width, length, and thickness of ob-jects are converted into the meters unit at the time they were constructed. The seman-tic objects that represented the modulus of elasticity, density, Poisson’s ratio, tensileproportional limit, and ultimate strength of the ASTM A36 steel were created usingthe assertions about http://www.swscm.net/material-properties.owl#ASTM A36

queried from the knowledge base. Inside the knowledge base component, the material-properties.owl file, shown in Figure 5.7, was downloaded from www.swscm.net andOWL statements with the “hasTensileModulusOfElasticity,” “hasDensity,” “hasPoissons-Ratio,” “hasTensileProportionalLimit,” and “hasTensileStrength” as verbs were re-trieved. All material property objects were normalized into the SI units in the samefashion as the length objects.

The SA service next requested the Matchmaker for a service that matches itsideal mesher profile. The URI to the OWL-S description of the Mesher service wassuggested. The SA service then retrieved the WSDL grounding information from theOWL-S description. The Semantic SOAP message that contained the problem geom-

54

<analysisRequest xmlns="uri:ppc-client"

xml:base="uri:ppc-client">

<getMaxStress>

<geometry>

5 <width unit="http://std.swscm.org/units.owl#m">

1.00</width>

<length unit="http://std.swscm.org/units.owl#cm">

200</length>

<thickness unit="http://std.swscm.org/units.owl#

10 inch">0.25</thickness>

</geometry>

<material>http://www.swscm.net/material-

properties.owl#ASTM_A36</material>

<support>

15 <condition>http://std.swscm.org/structures.owl#

LongerEdgeCantileverSupport</condition>

</support>

<loading>

<code>http://std.swscm.org/structures.owl#

20 TH2527</code>

<condition>http://std.swscm.org/structures.owl#

LibraryFloor</condition>

<loadFactor>http://std.swscm.org/structures.owl#

ServiceLF</loadFactor>

25 </loading>

<resultUnit> http://std.swscm.org/units.owl#Pa</resultUnit>

</getMaxStress>

</analysisRequest>

Figure 5.6: Sample Request Message from PDA Client

etry was transmitted to the rendezvous URL of Mesher, and the eight-noded brickelement mesh was constructed.

After this, the SA service handled the boundary conditions (BCs). For displace-ment BCs, it created a semantic array object of prescribed displacements using asser-tions from the definition of the concept http://std.swscm.org/structures.owl#-

LongerEdgeCantileverSupport, which specified nodal displacements in terms of a setof nodes at a specific location (e.g., the set of nodes that “has X coordinate equal tozero”) using the “pinned” node and the “locked” displacement concepts. The OWLstatements that define such concepts are presented in Figure 5.8. The definition of thepinned node asserted that the displacements ux, uy, and uz for each node be specifiedas locked. The locked displacement was asserted as the “Scalar” that “has value” of0.00 and “has unit” of meters. The force BCs were handled similarly.

Later, the SA service requested the Matchmaker for a service that matches itsideal structural analysis profile. The ideal input parameters comprised a mesh, the forceand displacement boundary conditions, and the material properties. The ideal outputparameter was an array of stress tensors. Each parameter was supported by an onto-logical concept. The ideal effect of service was http://std.swscm.org/Structural-

AnalysisEffects.owl#SmoothStressAtIntegrationPoints. The same input andoutput parameters that match the ideal sets were described in the ParEFG and FEMservice. However, the FEM service gave the http://std.swscm.org/Structural-

AnalysisEffects.owl#StressAtIntegrationPoints effect, whereas the SmoothStress-AtIntegrationPoints, a subclass of StressAtIntegrationPoints, was given in theParEFG case. The matchmaking algorithm thus recommended the ParEFG service.The Semantic SOAP message was submitted to the rendezvous URL and the arrayof stress tensors at integration points were computed. The Von Mises stresses at the

55

<rdf:RDF>

...

<mat:DuctileMaterial rdf:ID="ASTM_A36">

<rdfs:label>ASTM A36 steel</rdfs:label>

5

<mat:hasDensity rdf:resource="#Steel_Density"/>

<mat:hasPoissonsRatio

rdf:resource ="#Steel_PoissonsRatio"/>

<mat:hasTensileModulusOfElasticity

10 rdf:resource ="#Steel_TensileModulusOfElasticity"/>

<mat:hasTensileProportionalLimit>

<mat:TensileProportionalLimit

rdf:ID="ASTM_A36_TensileProportionalLimit">

15 <mat:representedBy>

<mat:Scalar>

<mat:hasValue rdf:datatype="&double;">36.0

</mat:hasValue>

</mat:Scalar>

20 </mat:representedBy>

<mat:hasUnit rdf:resource="&unit;#ksi"/>

</mat:TensileProportionalLimit>

</mat:hasTensileProportionalLimit>

25 <mat:hasTensileStrength>

<mat:TensileStrength

rdf:ID="ASTM_A36_UltimateStrength">

<mat:representedBy>

<mat:Scalar>

30 <mat:hasValue rdf:datatype="&double;">58.0

</mat:hasValue>

</mat:Scalar>

</mat:representedBy>

<mat:hasUnit rdf:resource="&unit;#ksi"/>

35 </mat:TensileStrength>

</mat:hasTensileStrength>

</mat:DuctileMaterial>

...

40 </rdf:RDF>

Figure 5.7: OWL Definition of ASTM A36 Steel

integration points were calculated by the application logic of the SA and the Max Valueservice was consulted to find the maximum stress. Finally, the SA service identifiedthe point where the maximum stress occurred, and the stress value and the locationwas returned to the User Interface for presentation to the user, as shown in Figure 5.9.From the analysis, the maximum Von Mises stress in the plate was 0.254 × 109 Pa.It exceeded the yield stress of the specified ASTM A36 material, which, according toFigure 5.7, equals 36 ksi (0.249×109 Pa). Therefore, the configuration of the structureneeded to be revised for improved safety.

5.4 Summary

This chapter has presented an architecture framework, from the system devel-opment point of view, for the application of Semantic Web Services technology toaddress software interoperability problems in structural engineering. It provides theimplementational details which were identified but not yet addressed by general re-searchers that are necessary for the practical application of Semantic Web Services inscientific computing, particularly in computational mechanics.

Based on several software tools, which are products from ongoing research on

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<rdf:RDF>

<swscm:DisplacementSpecification rdf:ID="Locked">

<mat:representedBy>

5 <mat:Scalar>

<mat:hasValue

rdf:datatype="&double;">0.00</mat:hasValue>

</mat:Scalar>

</mat:representedBy>

10 <mat:hasUnit rdf:resource="&unit;#m"/>

</swscm:DisplacementSpecification>

<swscm:Support rdf:ID="Pinned">

<rdfs:label>Simple support condition</rdfs:label>

15

<swscm:hasDisplacementSpecification>

<swscm:onComponent rdf:resource="#ux"/>

<swscm:hasSpecification rdf:resource="#Locked"/>

</swscm:hasDisplacementSpecification>

20

<swscm:hasDisplacementSpecification>

<swscm:onComponent rdf:resource="#uy"/>

<swscm:hasSpecification rdf:resouce="#Locked"/>

</swscm:hasDisplacementSpecification>

25

<swscm:hasDisplacementSpecification>

<swscm:onComponent rdf:resource="#uz"/>

<swscm:hasSpecification rdf:resouce="#Locked"/>

</swscm:hasDisplacementSpecification>

30

</swscm:Support>

</rdf:RDF>

Figure 5.8: OWL Definition of Pinned Node

Figure 5.9: Output Screen on PDA Client

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the Semantic Web and Semantic Web Services, a system prototype was developedto demonstrate how the proposed framework can help integrate multiple structuralengineering software modules—potentially developed by various groups of programmersand deployed on heterogeneous platforms—with the element-free Garlerkin methodsoftware on a parallel computer cluster and the ANSYS finite element analysis softwarepackage on a Windows PC as particular examples. In addition, the system prototypealso demonstrates how ubiquitous access to computational facilities can be achievedthrough personal digital assistant and smartphone devices that support Web Servicestechnology. In this aspect, the proposed framework could be especially useful forengineers at construction sites.

The proposed framework will become more useful as Web Services technol-ogy advances. As a platform-independent technology, Web Services allows convenientaccess to commercial software and potentially is an effective means both to reducepractitioners’ cost (through a pay-per-use model) and to prevent software piracy (asexecutable codes of Web services are located on remote servers and so are hidden fromusers). Thus, it is envisaged that general computational mechanics and structuralengineering software developers will gradually adopt Web Services as their architec-tural platform, and the proposed framework will be a practical tool to assist in theintegration of Web service software components.

Despite the advantages of the proposed framework and the example implemen-tation, there are some issues that have not been addressed. In particular, issues relatedto trust and security have not been taken into account explicitly. More research workis also required to ensure that premises from ontological sources and results from ser-vice providers are credible and can be used in mission-critical environments. Financialaspects, such as the potential implementation of a pay-per-use model, were not specifi-cally implemented. However, concepts from e-commerce can be adopted and integratedinto the framework. Last but not least, as the framework depends primarily on on-tologies, a more complete body of knowledge related to computational mechanics andstructural engineering needs to be captured and published on the Web, and the wideadoption of such ontologies from software developers are essential for the frameworkto work at its full potential.

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CHAPTER 6

CONCLUSIONS

6.1 Conclusion and Discussion

This dissertation has proposed a structural engineering support system and twoframeworks for the application of semantic computing—by means of metadata andontologies—to improve the productivity of structural engineers on engineering designprojects. The first system architecture framework, the Blog and Digital Library Frame-work for Engineering Knowledge Management (Blog+DL), is based on three softwarecomponents that enable a methodology which uses Weblogs and Digital Libraries asbuilding blocks for a collaborative engineering knowledge management system to assistengineers in sharing, exchanging, and improving their knowledge, skills and computingresources. The second system architecture framework proposed is the Semantic WebServices Framework for Computational Mechanics (SWSCM). The software compo-nents required for it are specified and the methodology by which they interact to unifyand utilize the scattered wealth of computing resources available is described.

The Blog+DL and SWSCM complement each other in the engineering process.Blog+DL relies on SWSCM to integrate computing resources shared by individualengineers. SWSCM can use Blog+DL to disseminate and discover shared computingresources, as well as to educate and exchange peer opinions about their underlying the-ories and user experiences. Two prototype systems were developed to demonstrate theproposed frameworks. The first system illustrates the joint application of Blog+DLand SWSCM to build support systems that facilitate the computationally orientedKM process in structural engineering, and the second system illustrates the full po-tential of SWSCM in facilitating a computationally intensive workflow that involvesheterogeneous software components.

To the best of the author’s knowledge, there have been no attempts to simultane-ously address the computational and knowledge management aspects of the engineeringprocess. Existing work in engineering literature that is closely related to this researchconsists of Liu et al. (2005), Peng and Law (2004), El-Tayeh and Gil (2007), Mezheret al. (2005), and Rezgui (2006). The first two address computational aspects of theengineering process and the latter three address knowledge management. A summarycomparison of their features with the ones of SWSCM and Blog+DL is provided inTable 6.1.

Peng and Law (2004) propose an Internet-based framework which facilitates thecomposition of distributed Web services into a finite-element analysis (FEA) program.By adopting a service-oriented system architecture (He, 2003), as SWSCM does, theframework allows new and legacy codes from heterogeneous sites to be dynamicallydiscovered, bound, and executed by the FEA program, and support for mobile deviceclients, such as Palm OS and Windows Mobile PDAs or smartphones, is technicallypossible. Their approach differs from the one in this research in that it is based onthe Java Remote Method Invocation (RMI) interoperation technology which predatesthe World Wide Web Consortium (W3C) Web Services standard; limiting interoper-ability with Web services on the Internet, and restricting the supported programminglanguage to Java. The dynamic service composition and data interoperability issueswere addressed by Liu et al. (2005), a group affiliated with Peng and Law, when theyproposed the Flow-based Infrastructure for Composing Autonomous Services (FICAS),an infrastructure for the composition of loosely coupled software components with an

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Table 6.1: Features Comparison with Existing Work

Blog+DL &SWSCM

Liuet al.(2005)

PengandLaw

(2004)

El-Tayehand Gil(2007)

Mezheret al.(2005)

Rezgui(2006)

KNOWLEDGE SHARING

Documents1 Y – – Y2 Y YComputing tools- Worksheets Y – – – Y –- Individual software Y – – – Y –- Subroutines Y – – – – –Multimedia content Y – – – Y3 Y3

Socialization Y – – Y – –

KNOWLEDGE ARCHIVING

Documents1 Y – – Y2 Y YIndividual software Y – – – Y –Multimedia contents Y – – – Y3 –Web content Y – – – – –Repository Y – – Y Y YAutomated review workflow Y – – – – –

KNOWLEDGE RETRIEVAL

Ontology-assisted Y – – Y – YKeyword tagging Y – – Y – YSmart keyword suggestion Y – – – – YMetadata search Y – – – – –Full-text search Y – – – – YCategorized browsing Y – – Y Y Y

USER INTERFACE & INTEROPERABILITY

User interface Web – – Web Web WebSupport for Social Network Analysis Y – – Y – –

System interoperability standard(s)URL,

RSS/Atom,OAI-MHP

– – ProprietaryWindows

filessharing

WebServices4

COMPUTATIONAL FEATURES

Support for mobile device clients Y Y Y3 – – –Service-oriented architecture Y Y Y – – –Support for legacy codes Y Y3 Y – – –Heterogeneous platform interoperability Y Y Y – – –Dynamic service discovery and binding Y Y Y – – –Dynamic service composition Y Y – – – –Dynamic data interoperability Y Y – – – –

Knowledge-base data sourceOWL

ontologies– – – – –

Interoperability standardsWeb Services& Semantic

Web5

ProprietaryJavaRMI

– – –

Supported programming languageJava & majorlanguages6

Java Java – – –

Compatible with existing Web Servicestechnology

Y – – – – –

1 including AutoCAD, Excel, PDF, PowerPoint, and Word

2 plain-text only

3 technically possible, although not explicitly discussed by the authors

4 SOAP, WSDL, UDDI

5 HTTP, SOAP, WSDL, RDF, OWL, and OWL-S

6 including C++, C#, Perl, PHP, Python, and VB.NET

60

active data mediation facility. Despite the reported superior performance over SOAPin high data volume engineering applications, the communication protocol and the ser-vice description scheme in FICAS are proprietary and not compatible with the W3Cstandards, making them incompatible with the emerging development tools in manyprogramming languages that conform to the W3C specifications. Ontologies and se-mantic descriptions of terms and Web services were not taken into account in theseproposals, so that Internet-based artificial intelligence technologies that allow moreseamless integration of software components from the Web, cannot be readily incorpo-rated.

Mezher et al. (2005) present a methodology to build a knowledge managementsystem in an international engineering consultant firm by using the Microsoft Windowsfile-sharing system on the company’s intranet, covering three main offices in differentcountries. Each office hosts a file-repository server that stores standard design pro-cedures, templates, forms, and computer programs, as well as lessons from sites andproject documents. The documents include Word and Excel files as well as AutoCADdrawings. Using the Internet Explorer Web browser as a front-end user interface, userscan retrieve pieces of knowledge from the repository by browsing through hierarchies offolders to which they are granted access privileges. Knowledge and computer programsare exchanged among users in different offices through e-mail but need to be autho-rized by a superior engineer before they can be stored on a file server. Their approachdiffers from the one in this research in that it lacks an integrated set of socializa-tion tools which facilitate the sharing and developing of tacit knowledge among users.The hierarchical browsing through explicit knowledge, without support from advancedmetadata and full-text search facilities, will make it difficult for users to precisely re-trieve relevant knowledge as the size of the repository grows. The system also lacksautomated support for the review workflow which increases the time required for criti-cal pieces of knowledge to gain approval for publication in the repository. The sharingof computational software tools in the form of Web Services is also not supported.

An advanced ontology-assisted knowledge retrieval technique for the construc-tion industry, with document summarization and full-text search capabilities, is pro-posed in Rezgui (2006); however, review workflow, metadata search, and socializationtechniques are not included in this study. Digital socialization in a construction teamwas addressed in El-Tayeh and Gil (2007); nevertheless, the sharing, archiving, andretrieval of knowledge are limited to plain-text documents.

Like Blog+DL, Rezgui (2006) is based on widely-accepted system interoperabil-ity standards. Their developments were however aimed at different applications. TheW3C standard compliant Web services adopted in Rezgui (2006) support interoper-ability at the application programming interface level, which is intended specifically forsoftware developers; whereas the RSS/Atom and the OAI-MHP features in Blog+DLsupport interoperability at the information exchange and consumption level, which isintended both for developers and general users.

6.2 Recommendations for Further Research

The support system as well as the SWSCM and Blog+DL frameworks proposedin this dissertation can potentially be applied not only to structural engineering butalso to other computationally-oriented application areas. The implementation of theprototype systems has spawned many interesting research topics worth further inves-tigation.

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In particular, this dissertation has focused on methodologies to more seamlesslyintegrate computational services owned or developed by different groups of people onheterogeneous computing platforms via the Internet or an intranet. One assumptionfor such integration is that all services are trustworthy and a second is that they areaccessible free of charge. Issues related to trust, security, and tariffs have not beenaddressed by this research. Such conditions, however, are not easily achieved when alarge number of services are deployed and consumed via the Internet, as is clear fromcurrent concerns about malicious software and Web phishing incidences. The trust,security, and tariff issues of Web services therefore need to be further investigated, inaddition to the refinement of the matchmaking and composition algorithms.

As the framework depends primarily on ontologies, more complete bodies ofknowledge in the target application domains of computational mechanics and structuralengineering need to be captured and published on the Web. The wide adoption ofsuch ontologies by software developers is essential for the proposed frameworks to beexploited to their full potential. The Blog+DL framework features an ontology-assistedkeyword suggestion component which assists users in providing proper metadata for theeffective and convergent retrieval of knowledge. A relatively simple keyword detectionalgorithm has been implemented in the prototype system for illustrative purposes.More advanced keyword detection techniques however, are available in the naturallanguage processing literature. Such techniques should be further explored to improvethe performance of metadata enforcement and knowledge retrieval. Last but not least,the social networking feature of Weblogs opens Blog+DL to research in social networkanalysis which holds the potential to further enrich the capabilities of the Blog+DL-based knowledge management systems.

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