semantic web: state-of-art and opportunities “industrial ontologies” group university of...
Post on 21-Dec-2015
213 views
TRANSCRIPT
Semantic Web:Semantic Web:State-of-Art and OpportunitiesState-of-Art and Opportunities
“Industrial Ontologies” Group
http://www.cs.jyu.fi/ai/OntoGroup/index.html
University of Jyväskylä, August 2003
Industrial Ontologies GroupIndustrial Ontologies Group
Our Team: “Industrial Ontologies” Group
• Head:– Vagan Terziyan
• Researchers:– Oleksandr Kononenko– Andriy Zharko– Oleksiy Khriyenko– Olena Kaykova– …
• Consultant (Metso Oy):– Jouni Pyotsia
• Manager (Science Park):– Mikko Kovalainen
MIT Department, University of Jyväskylä
“Industrial Ontologies” Group: http://www.cs.jyu.fi/ai/OntoGroup/index.html
““Industrial Ontologies” Group: Industrial Ontologies” Group: Our HistoryOur History
• 1978-1984 – We took part in development of the first in USSR Industrial Natural Language Processing System “DESTA”, which included semantic analysis and ontologiesontologies;
• 1985-1989 - We took part in development of the first in USSR Industrial Automated Natural Language Programming System “ALISA”, which Enabled Semantic AnnotationEnabled Semantic Annotation, DiscoveryDiscovery and IntegrationIntegration of software components (prototype of today's Semantic Web ServicesSemantic Web Services concept);
““Industrial Ontologies” Group: Industrial Ontologies” Group: Our HistoryOur History
• 1990-1993 – under name of Metaintelligence Lab. we were piloting concept of a Metasemantic Network (triplet-based (meta-)knowledge representation model) – prototype of today’s RDF-based knowledge representation in Semantic WebSemantic Web;
• 1994-2000 – various projects with industrial partners, e.g. MetaAtom – “Semantic Diagnostics of Ukrainian Nuclear Power Stations based on Metaknowledge”; MetaHuman – industrial medical diagnostics expert system based on Metaknowledge”; Jeweler – metamodelling and control of industrial processes, etc.; got several research grants from Finnish Academy;
““Industrial Ontologies” Group: Industrial Ontologies” Group: Our HistoryOur History
• 2000-2001 – we have created branches in Vrije Universiteit Amsterdam (heart of Semantic Web activities in Europe) where now working 5 our former team members, in Jyvaskyla University (several tens of researchers) and established research groups in Kharkov (Ukraine) on Data Mining, Educational Ontologies, Telemedicine, etc.
• 2001-2003 – we took part in MultiMeetMobile Tekes Project, in InBCT Tekes Project in Tempus EU Compact Project in (or in cooperation with) University of Jyvaskyla where we further promote Semantic Semantic WebWeb concepts.
Industrial Ontologies Group:Industrial Ontologies Group:Important ObjectiveImportant Objective
• For us there are no doubts about the possibilities, which Semantic Web opens for industry.
• that is why one important objective of our activities is to study appropriate industrial cases, collect arguments, launch industrial projects and develop prototypes for the industrial companies to not only believe together with us but also benefit from the Semantic Web.
Why and Where Semantic Web ?Why and Where Semantic Web ?
WWW
Business
Knowledge Management
more then 3,000,000,000 web-pages “Information” burst ICT needs comprehensive resource management technology
Needs for integration of businesses Web Services for e-Business Standardization and Interoperability problems
Consolidate and reuse experience Standardize knowledge sharing technology Needs for the intelligent tools to use human’s knowledge
4
Web Limitations
Doubles in sizeevery six months
Average WWW searches examineonly about 25% of potentially
relevant sites and return a lot ofunwanted information
Information on web is not suitablefor software agents
World Wide Web
Semantic Web
The Semantic Web is avision: the idea of havingdata on the Web defined andlinked in a way that it can beused by machines not just fordisplay purposes, but forautomation, integration andreuse of data across variousapplications.
7
B e f o r e S e m a n t i c W e b
W e b c o n t e n t
U s e r sC r e a t o r sW W Wa n dB e y o n d
8
S e m a n tic W e b S tru c tu re
S e m a n ticA n n o ta tio n s
O n to lo g ie s L o g ic a l S u p p o rt
L a n g u a g e s T o o ls A p p lic a tio n s /S e rv ic e s
W e b c o n te n t
U se rsC re a to rsW W Wa n dB e y o n d
S e m a n ticW e b
Motivation for Semantic Web Motivation for Semantic Web
What is the “Transactional Web”
• Today: “The eye-ball Web” - the architecture of the Web is geared towards delivering information visually.
• Tomorrow: “The transactional Web” – the architecture of the Web geared towards intelligently exchanging information between applications.
Summarizing the Problem: Computers don’t understand Meaning
• “My mouse is broken. I need a new one…”
An ExampleUse of ontology
“My mouse is broken” vs. “My mouse is dead”
Approach: Semantic WebApproach: Semantic Web
“The Semantic Web is a vision: the idea of having data on the Web defined and linked in a way that it can be used by machines not just for display purposes,
but for automation, integration and reuse
of data across various applications”
http://www.w3.org/sw/
The Semantic Web is an initiative with the goal of extending the current Web and facilitating Web automation, universally accessible web resources, and the 'Web of Trust', providing a universally accessible platform that allows data to be shared and processed by automated tools as well as by people.
Word-Wide Correlated ActivitiesWord-Wide Correlated Activities
Semantic Web
Grid Computing
Web Services
Agentcities
Global, collaborative effort to construct an open network of on-line systems
hosting diverse agent based services.
Providing technologies for automated communication,discovery and integration of Web services,
to enable on-the-fly software composition throughthe use of loosely coupled, reusable software components.
FIPA
Producing standards for the interoperation of heterogeneous software agents.
Extending current web by giving informationa given well-defined meaning, better enablingcomputers and people to work in cooperation
Utilizing the global Internet to builddistributed computing and communications
infrastructures.
HTML
100%
50%
0%
XML
DAML+OIL
2000 2005 2010
“Fifty percent of the content on the Web will be in XML format by the end of 2003” ……….Gartner Group
“In 30 years e-commerce will have become second nature. Lifelike, intelligent virtual assistants will be performing most routine transactions and simple negotiations electronically on our behalf. More technological change will have taken place in that period than during the entire twentieth century, and the curve will continue to steepen exponentially into the foreseeable future.” Ray Kurzweil
Web Migration to New TechnologyWeb Migration to New Technology
Tim Berners-Lee's Vision of Semantic Web (IJCAI-01)
Semantic Web: New “Users” Semantic Web: New “Users”
SemanticAnnotations
Ontologies Logical Support
Languages Tools Applications /Services
Web content
UsersCreatorsWWWandBeyond
SemanticWeb
Semantic Webcontent
UsersSemanticWeb andBeyond
Creators
applications
agents
Content
Agents Annotations
Ontologies
Software engineersOntology engineers
Web designers
Content creators
Logic, Proof and Trust
AI Professionals
Mobile Computing Professionals
Professions around Semantic WebProfessions around Semantic Web
Semantic Web: Resource IntegrationSemantic Web: Resource Integration
Shared ontology
Web resources / services / DBs / etc.
Semantic annotation
Semantic Web: What to Annotate ?Semantic Web: What to Annotate ?
Web resources / services / DBs / etc.
Shared ontology
Web users (profiles,
preferences)
Web access devices
Web agents / applications
External world resources
Smart machines and devices
The Semantic Web
Can’t we just use XML?This is what a web-page in natural language looks like for a machine
J. Hendler
J. Hendler
XML helps
CV
name
education
work
private
< >
< >
< >
< >
< >
XML allows “meaningful tags” to be added toparts of the text
J. Hendler
XML machine accessible meaning
CV
name
education
work
private
< >
< >
< >
< >
< >
< >
< >
<>
<>
<>
But to your machine, the tags look like this….
J. Hendler
Schemas take a step in the right direction
Schemas help….
CV
name
education
work
private
< >
< >
< >
< >
< >
< >
< >
<>
<>
<>
CV
name
education
work
private
< >
< >
< >
< >
< >
< >
< >
<>
<>
<>
< > …by relating common termsbetween documents
But other people use other schemas
CV
name
education
work
private
< >
< >
< >
< >
< >
< >
>
<>
<>
Someone else has one like this….
J. Hendler
The “semantics” isn’t there
CV
name
education
work
private
< >
< >
< >
< >
< >
< >
< >
<>
<>
<>
CV
name
education
work
private
< >
< >
< >
< >
< >
< >
< >
<>
<>
<>
< >…which don’t fit in
CV
name
education
work
private
< >
< >
< >
< >
< >
< >
< >
< >
< >
J. Hendler
KR provides “external” referents to merge on
CV
name
education
work
private
< >
< >
< >
< >
< >
< >
< >
<>
<>
<>
CV
name
education
work
private
< >
< >
< >
< >
< >
< >
< >
<>
<>
<>
CV
name
education
work
private
< >
< >
< >
< >
< >
< >
< >
<>
<>
<>
Semantic Web languages add mappings and structure.
CV
name
education
work
private
< >
< >
< >
< >
< >
< >
< >
< >
< >
CV
name
education
work
private
< >
< >
< >
< >
< >
< >
< >
< >
< >
CV
name
education
work
private
< >
< >
< >
< >
< >
< >
< >
< >
< >
CV
name
education
work
private
< >
< >
< >
< >
< >
< >
< >
< >
< >
CV
name
education
work
private
< >
< >
< >
< >
< >
< >
< >
< >
< >
J. Hendler
Semantic Web basics…
RDF:
• is a W3C standard, which provides tool to describe Web resources
• provides interoperability between applications that exchange machine-understandable information
RDF Schema:– is a W3C standard which defines vocabulary for RDF– organizes this vocabulary in a typed hierarchy – capable to explicitly declare semantic relations between
vocabulary terms
Ontological Vision of Semantic Web
Semantic Web needs ontologies
An ontology is document or file that formally and in a
standardized way defines the hierarchy of classes within the domain, semantic relations among terms and inference rules
Use of ontologies: Sharing semantics of your data across
distributed applications
Ontologies: the foundation of Semantic WebOntologies: the foundation of Semantic Web
Document
Location
Subject
name
is-a
uri
comment __Thing__
is-a
Report
Web-page
Access Rights
Author
http://www.ontogroup.net
is-a
\\AgServ\vagan\InBCT_1.doc
V. Terziyan
Author
O. Kononenko
Author
uriLocation
draft
comment
public
Home page
comment
3.1: analysis
Subject
Instance-of Instance-of
Query 1: get all documents from location X, but not web-pagesQuery 2: get documents related to Y, with more then one author, one of which is TerziyanQuery 3: are there web-pages of Z with “private” access related to documents with subject S?
Related to
Related to
Access rights
#doc1 #doc2
Ontologies are key enabling technology for the Semantic Web
“..explicit specification of conceptualization..”
Ontology is formal and rich way to provide shared and common understanding of a domain, that can be used by people and machines
Semantic Webname
public
private
Semantic Web: InteroperabilitySemantic Web: InteroperabilityOntology A: Documents Ontology B: Research
A commitment to a common ontology is a guarantee of aconsistency and thus possibility of data (and knowledge) sharing
Common (shared) ontology
Ontology C: Services
System 1System 2
\\AgServ\vagan\InBCT_1.doc
V. Terziyan
A:Report
A:Location3.1: analysis
A:Subject
A:Author
Instance-ofSemantic Web
A:name
Co-operative Work in WebCo-operative Work in Web
WWW
Co-operative Work in Semantic WebCo-operative Work in Semantic Web
WWW
Semantic Web
Semantic Web is not Only ...Semantic Web is not Only ...
… but Alsobut Also ...
Enterprise Integration Technologies
• Web Service Technology (SOAP, WSDL and UDDI);• Enterprise Integration (Enterprise Application Integration
and E-Commerce in form of Business-to-Business Integration as well as Business-to-Consumer);
• Semantic Web Technology (ontology languages).
The promise is that Web Service Technology in conjunction with Semantic Web Technology (“Semantic Web Services”) will make Enterprise Integration dynamically possible for all types and sizes of enterprises compared to the “traditional” technologies
The Web Services Stack
Wire Protocol Description Discovery
SOAP WSDL Registry (UDDI)
provides a provides a standard, flexible standard, flexible communications communications
channelchannel
provides a provides a standard, flexible standard, flexible way to describe way to describe what and how a what and how a
Web service does Web service does what it doeswhat it does
provides a standard, flexible provides a standard, flexible way to discover where a way to discover where a
Web service is located and Web service is located and where to find more where to find more
information about what the information about what the Web service doesWeb service does
interoperability at interoperability at the lowest levelthe lowest level
interoperability at interoperability at the content levelthe content level
dynamic dynamic discoverydiscovery
Six Challenges for the Semantic Web
Richard Benjamins, Jesus Contreras,
Oscar Corcho, Asuncion Gomez-Perez
April 2002
• Semantic Web content is a content annotated according to particular ontologies, which define the meaning of the words or concepts appearing in the content.
• Currently, there is little Semantic Web content available. Researchers are building tools to support semantic annotation. However, they have two limitations:
1. Most of them annotate only static pages, and
2. Many of them focus on creating new content.
• There is a need need to create a set of annotation services (middleware) concerning static and dynamic web documents, which may include multimedia, and web services.
Challenge 1: Availability of ContentChallenge 1: Availability of Content
• Constructing of kernel ontologies to be used by all the domains. E.g. IEEE Standard Upper Ontology Group aims to create a common unified top level ontology, also RosettaNet, etc.
• Providing methodological and technological support for most of the activities of the ontology development process.
• Managing evolution of ontologies and their relation to already annotated data. Configuration management tools are necessary to keep control of the versions of each ontology as well as the interdependencies between them and annotations.
Challenge 2: Ontology Availability, Challenge 2: Ontology Availability, Development and EvolutionDevelopment and Evolution
• Once we have the Semantic Web content, we need to worry about how to manage it in a scalable manner, that is, how to organize it, where to store it and how to find the right content:
• Storage and organization of Semantic Web pages. The ‘basic’ Semantic Web consists of ontology-based annotated pages whose linking structure reflects the structure of the WWW, that is, pages connected to others by means of hyperlinks. This hyperlinked configuration does not fully exploit the underlying semantics of Semantic Web pages. We foresee the use of semantic indexes to group Semantic Web content based on particular topics. Semantic indexes will be generated dynamically using ontological information and annotated documents.
• Finding of information in the Semantic Web. A mechanism of coordination among semantic indexes must be provided for the easy finding of SW content taking into account the semantics of web resources. A peer to peer architecture could be explored, similar to the current configuration of routers in the WWW. Indexes could be considered as active agents that know what topics they can handle. Topics that do not occur in the index are semantically routed to neighbour indexes. The use of agents should be explored for negotiation techniques in order to
obtain the semantic routing of topics.
Challenge 3: Scalability of Semantic Challenge 3: Scalability of Semantic Web ContentWeb Content
• Multilinguality plays an increasing role at the level of ontologies, of annotations and of user interface: • At the ontology level, ontology builders may want to use their native
language for the development of the ontologies in which annotations will be based.
• At the annotation level, annotation of content can be performed in various languages. Since more users (especially content providers) will rather annotate content than develop ontologies, proper support is needed that allows annotating content in their native language.
• At the user interface level, millions of people would like to access relevant content in their native language irrespective of the source language in which annotations are presented. Any Semantic Web approach should include facilities to access information in several languages. Internationalisation and localization techniques should be explored to personalize information access based on the native language of the user.
Challenge 4: MultilingualityChallenge 4: Multilinguality
• With the increasing amount of information overload, intuitive visualization of content will become more and more important, as users will be increasingly demanding easy recognition of the relevance of content for their purposes.
• The use of semantic indexes and routers for the storage, organization and finding of information, will require a major step forward in visualization, compared to traditional site maps that represent link structures.
• Techniques should allow for three-dimensional and new visualisation techniques to visualise SW content in any of the current SW languages. Technologies to be considered include X3D (of the Web3D Consortium), Java3D (API for writing programs to display and interact with three-dimensional graphics, Shockwave3D (technology introduced by Macromedia).
Challenge 5: VisualizationChallenge 5: Visualization
• The Semantic Web is an emerging field and the WWW consortium is producing recommendations on the languages and technology that will be used in this area.
• In order to advance the state of the art in the Semantic Web, it is important that such standards appear fast and will be adopted by the community.
Challenge 6: Semantic Web Challenge 6: Semantic Web Language StandardizationLanguage Standardization
Architecture of the Semantic Web Architecture of the Semantic Web TechnologyTechnology
Semantic Web Companies Semantic Web Companies (samples)(samples)
Profium (www.profium.com) develops Semantic Content Management Systems based on RDF Metadata and XML.
OntologyWorks brings ontology-based information and enterprise software engineering tools to the commercial market.
NetworkInference creating software products, and promoting the development of web standards, that, together, will power the advance of machine understanding, and reduce the level of human processing involved in web-based applications.
CognIT is the Norway-based provider of CORPORUM, a tool suit for Ontologie Extraction, Semantic and Content Analysis, Summarising and Content Visualisation.
Taalee provides semantics based search facilities.
Invention-Machines provides also semantics based search facilities.
AIdministrator develops semantic classification tools, plus software to visualise the results of semantic searches.
Ontoprise develops Ontology Editors and Inference Engines.
Intellidimension provides an RDF based information integration environment including an inference engine.
Summary:Summary:
Semantic WebSemantic Web Concept & Applications Concept & Applications(according to Dieter Fensel)(according to Dieter Fensel)
URI, HTML, HTTPStaticWWW
500 million usermore than 3 billion pages
Concept
URI, HTML, HTTPStaticWWW
Serious Problems in information•finding
•extracting•representing•interpreting
•and maintaining
RDF, RDF(S), OWLSemantic Web
Concept
Static
Dynamic
Bringing the computer back as a device for computation
URI, HTML, HTTP RDF, RDF(S), OWL
WWW Semantic Web
UDDI, WSDL, SOAP
Web Services
Concept
Bringing the web to its full potential
Static
Dynamic UDDI, WSDL, SOAP
Web Services
URI, HTML, HTTP RDF, RDF(S), OWL
WWW Semantic Web
Intelligent Web Services
Concept
Concept
• The semantic web is based on machine-processable semantics of data.
• Its backbone technology are Ontologies.
• It is based on new web languages such as XML, RDF, and OWL, and tools that make use of these languages.
• Ontologies are key enabling technology for the semantic web.
• They interweave human understanding of symbols with their machine processability.
• In a nutshell, Ontologies are formal and consensual specifications of conceptualizations that provide a shared and common understanding of a domain.
Concept
Applications
• Knowledge Management
• Enterprise Application Integration
• eCommerce
Knowledge Management
• The competitiveness of companies in quickly changing markets depends heavily on how they exploit and maintain their knowledge.
• Increasingly, companies realize that their intranets are valuable repositories of corporate knowledge.
• To deal with this, several document management systems entered the market. However, these systems have severe weaknesses.
Knowledge Management
• Searching information: Existing keyword-based search retrieves irrelevant information that uses a certain term in a different meaning, and misses information when different terms with the same meaning about the desired content are used.
• Extracting information: Currently, human browsing and reading is required to extract relevant information from information sources and they need to manually integrate information spread over different sources.
Knowledge Management
• Maintaining weakly structured text sources is a difficult and time-consuming activity when such sources become large. Keeping such collections consistent, correct, and up-to-date requires mechanized representations of semantics that help to detect anomalies.
• Automatic document generation would enable adaptive websites that are dynamically reconfigured according to user profiles or other aspects of relevance.
Knowledge Management
• The Semantic Web will provide much more automated services based on machine-processable semantics of data, and on heuristics that make use of these metadata.
• Currently, we see many projects and products that are close to the market employing such concepts and ideas.
Enterprise Application Integration
• The integration of data, information, knowledge; processes; applications; and business becomes more and more important.
• Therefore, the Enterprise Application Integration area will have soon a major share of the overall spent IT expenses.
• A number of reasons are responsible for this trend.
• Up to now, many companies trying to solve their integration needs by adhoc integration projects, however, adhoc integration do not scale.
• Therefore, after a phase of adhoc integration companies start to search for the Silver bullet that may help to solve the growing problem.
• However, global integration requires serious investments and time.
Enterprise Application Integration
• A successful integration strategy must combine the advantages of adhoc and global integration strategies:
– Learning from adhoc integration means to make sure that we must reflect business needs as the driving force for the integration process;
– Learning from global integration means to make sure that we must create extendable and reusable integrations.
Enterprise Application Integration
• Purpose-driven. We need to identify the major integration needs in terms of business processes and to structure our integration efforts around these needs.
• Extendable. We use Ontologies for publishing the information of data sources and for aligning it with business needs. By using Ontologies for making information explicit we ensure that our integration efforts can be extended in response to new and changed business needs.
• Reusable: Use web service technology to reflect further integration needs based on standardization. Web services as a vendor and platform independent software integration platform are of critical importance.
Enterprise Application Integration
• We expect that Enterprise Application Integration will be the major application are of Semantic Web technology before it will take the next logical step:
=> the integration of several organizations, i.e., eCommerce.
Enterprise Application Integration
eCommerce
• eCommerce in business to business (B2B) is not a new phenomenon.
• However, the automatization of business transactions has not lived up to the expectations of its propagandists.
• Establishing a eCommerce relationship requires a serious investment and it its limited to a predefined number of trading partners.
• Internet-based electronic commerce provides a much higher level of openness, flexibility and dynamics that will help to optimize business relationships.
• Anytime, anywhere, and anybody eCommerce provides completely new possibilities.
eCommerce
• Instead of implementing one link to each supplier, a supplier is linked to a large number of potential customers when he is connected to the marketplace.
• A supplier or customer can change its business relationships reflecting new demands from his market.
• This enables virtual enterprises and vica versa it enables to brake large enterprises up into smaller pieces that mediate their eWork relationship based on eCommerce relationships.
eCommerce
• However, enabling flexible and open eCommerce has to deal with serious problems.
• Heterogeneity in the product, catalogue, and document description standards of the trading partner.
• Effective and efficient management of different styles of description becomes a key obstacle for this approach.
eCommerce
• Openness of eCommerce cannot be achieved without standardization.
• This we can learn from the web!
• Here, we also require standardization of the actual content, i.e., we require Ontologies.
eCommerce: Opennes
• Flexibility of eCommerce cannot be achieved without multi-standard approaches.
• Ontology need to be implemented as networks of meaning where from the very beginning, heterogeneity is an essential requirement for this Ontology network.
• Tools for dealing with conflicting definitions and strong support in interweaving local theories are essential in order to make this technology workable and scalable.
eCommerce: Flexibility
• Dynamic of eCommerce requires standards that act as living entities.
• Products, services, and trading modes are subject of high change rates.
• Ontologies are used as a means of exchanging meaning between different agents.
• They can only provide this if they reflect an inter-subjectual consensus.
• By definition, they can only be the result of a social process.
eCommerce: Dynamic
– For this reason, Ontologies cannot be understood as a static model.
– An Ontology is as much required for the exchange of meaning as the exchange of meaning may influence and modify an Ontology.
– Consequently, evolving Ontologies describe a process rather than a static model.
– Ontologies must have strong support in versioning and must be accompanied by process models that help to organize evolving consensus.
eCommerce: Ontologies
Summary: Risc vs. Impact Tradeoff
Impact
Risc
Knowledge Management
Enterprise Application Integration
eCommerce
Heterogeneity...Heterogeneity... … … is a Babel Tower!!is a Babel Tower!!
SEMANTIC INTEROPERABILITYSEMANTIC INTEROPERABILITY
metadata
ontologies
contexts
SEMANTIC HETEROGENEITYSEMANTIC HETEROGENEITY
• The first Semantic Web Kick-Off Meeting in Finland was in Helsinki 2 November 2001;
• Later Finnish portal on Semantic web activities was launched in http://www.cs.helsinki.fi/u/eahyvone/stes/semanticweb.
• Semantic Computing (SeCo) research group was formally established in the spring 2002. The group belongs to the University of Helsinki, Department of Computer Science and Helsinki Institute for Information Technology (HIIT). Group leader is Prof. Eero Hyvonen
• The first projects focus on Semantic Web and Web Service applications and representation of cultural content on the Web.
Semantic Web Activities in Finland
Industrial Ontologies GroupSamples of our Research:
“Applications of Semantic Web”
Web Resource/Service Integration:Web Resource/Service Integration:Server-Based Transaction MonitorServer-Based Transaction Monitor
Server Client
Server
Webresource /
service
Webresource /
service
Transaction Service
TMTM
wireless
Web Resource/Service Integration:Web Resource/Service Integration:Mobile Client-Base Transaction Mobile Client-Base Transaction
MonitorMonitor
ServerClient
Server
Webresource /
service
TM
Webresource /
service
wireless
wireless
The conceptual scheme The conceptual scheme of the ontology-based of the ontology-based
transaction transaction management with management with multiple e-servicesmultiple e-services
Transaction data
Service 1 ********
Service 2 ********
…
Service s ********
Services data
Transaction monitor
Client 1
…
Service 1 ********
Service 2 ********
…
Service s ********
Services data
Transaction monitor
Client r
Parameter 1
Parameter 2
…
Parameter n
Recent value
Recent value
…
Recent value
Transaction data
Parameter 1
Parameter 2
…
Parameter n
Recent value
Recent value
…
Recent value
Service atomic action ontologies
Parameter 1
Parameter 2
…
Parameter n
Parameter ontologies
Ontologies
Name 1
Name 2
…
Name n
Default value / schema 1
Default value / schema 2
…
Default value / schema n
Name of action 1
input parameters
output parameters
Name of action 2
input parameters
output parameters
Name of action k
input parameters
output parameters
…
Service Tree
Client 1 ********
Client 2 ********
…
Client r ********
Clients data
Subtransaction monitor
Service 1
Service Tree
Client 1 ********
Client 2 ********
…
Client r ********
Clients data
Subtransaction monitor
Service s
…
Terziyan V., Ontological Modelling of E-Services to Ensure Appropriate Mobile Transactions, In: International Journal of Intelligent Systems in Accounting, Finance and Management, J. Wiley & Sons, Vol. 12, 2003, 14 pp.
Ontology-Based Transaction Ontology-Based Transaction Management for the Semantic WebManagement for the Semantic Web
Consider two basic transaction management architectures in mobile environment depending on where the Transaction Monitor (TM) will be located. First one (Server-Based) assumes that TM will be located in server side, e.g. within some transaction management service. Second one (Client-Based) supposes that TM is located in mobile client terminal.
The first objective will be to provide and study an integrated mobile transaction management architecture for the Semantic Web applications, which will combine the best features from these two architectures by intelligent switching from one architecture to another one depending on current application context.
There is already some ontological support for Semantic Web resources and services interoperability based on OWL, DAML-S. However to be able to manage transactions in Semantic Web across multiple resources (or services) there will not be enough only ontologies for semantic annotations of these resources; there will be evident need of the ontology for the Semantic Web transactions itself.
The second objective will be developing pilot ontology for the RDF-based semantic annotation of mobile transactions in the Semantic Web.
21
Web Resource/Service Integration:Server-Based Transaction Monitor
Server Client
Server
Webresource /
service
Webresource /
service
Transaction Service
TM
wireless
22
Web Resource/Service Integration:Mobile Client-Base Transaction Monitor
ServerClient
Server
Webresource /
service
TM
Webresource /
service
wireless
wireless
Public merchants,public customers, publicinformation providers
…
…
Clients
SMOs
SMRs
Maps<path network>
Maps<business points>
Integration,Analysis,Learning
Businessknowledge
Server
I
C
I
I
S
I
Negotiation,Contracting,
Billing
Meta-Profiles
Profiles
XMLWML
LocationProviders
Server
Map ContentProviders
Server
ContentProviders
Server
…
…
…
ExternalEnvironment
XML
$$$ Banks
Architecture for a Mobile P-Commerce ServiceArchitecture for a Mobile P-Commerce Service
Terziyan V., Architecture for Mobile P-Commerce: Multilevel Profiling Framework, IJCAI-2001 International Workshop on "E-Business and the Intelligent Web", Seattle, USA, 5 August 2001, 12 pp.
BANK: P-Commerce Service providerBANK: P-Commerce Service provider
Personal ontologyPersonal ontology General ontologyGeneral ontologyAutomatic:Automatic:
Mapping and TransactionsMapping and Transactions
Service UserService User
Service UserService User
Service UserService User Service UserService User
Service UserService User
Service UserService User
via resources and users annotationsvia resources and users annotations
18
Multimeetmobile Project (2000-2001)
Information TechnologyResearch Institute(University of Jyvaskyla):Customer-oriented research anddevelopment in Information Technology
http://www.titu.jyu.fi/eindex.html
Multimeetmobile (MMM) Project(2000-2001):Location-Based Service System and TransactionManagement in Mobile Electronic Commerce
http://www.cs.jyu.fi/~mmm
Academy of FinlandProject (1999):Dynamic Integration ofClassification Algorithms
Mobile Location-Based Mobile Location-Based Service in Semantic WebService in Semantic Web
19
M-Commerce LBS systemhttp://www.cs.jyu.fi/~mmm
In the framework of the Multi Meet Mobile(MMM) project at the University of Jyväskylä,a LBS pilot system, MMM Location-basedService system (MLS), has been developed.MLS is a general LBS system for mobileusers, offering map and navigation acrossmultiple geographically distributed servicesaccompanied with access to location-basedinformation through the map on terminal’sscreen. MLS is based on Java, XML and usesdynamic selection of services for customersbased on their profile and location.
Virrantaus K., Veijalainen J., Markkula J.,Katasonov A., Garmash A., Tirri H., Terziyan V.,Developing GIS-Supported Location-BasedServices, In: Proceedings of WGIS 2001 - FirstInternational Workshop on Web GeographicalInformation Systems, 3-6 December, 2001, Kyoto,Japan, pp. 423-432.
2 0
A d a p t i v e i n t e r f a c e f o r M L S c l i e n t
O n l y p r e d i c t e d s e r v i c e s , f o r t h e c u s t o m e r w i t h k n o w n p r o f i l ea n d l o c a t i o n , w i l l b e d e l i v e r e d f r o m M L S a n d d i s p l a y e d a tt h e m o b i l e t e r m i n a l s c r e e n a s c l i c k a b l e “ p o i n t s o f i n t e r e s t ”
21
Route-based personalization
Static Perspective Dynamic Perspective 2 2
I n d u c t i v e l e a r n i n g o f c u s t o m e rp r e f e r e n c e s w i t h i n t e g r a t i o n o f p r e d i c t o r s
rrmrr yxxx ,...,, 21
S a m p l e I n s t a n c e s
tmtt xxx ,...,, 21
y t
L e a r n i n g E n v i r o n m e n t
P 1 P 2 . . . P n
P r e d i c t o r s / C l a s s i f i e r s
T e r z i y a n V . , D y n a m i c I n t e g r a t i o n o f V i r t u a l P r e d i c t o r s , I n : L . I . K u n c h e v a , F .S t e i m a n n , C . H a e f k e , M . A l a d j e m , V . N o v a k ( E d s ) , P r o c e e d i n g s o f t h e I n t e r n a t i o n a l I C S CC o n g r e s s o n C o m p u t a t i o n a l I n t e l l i g e n c e : M e t h o d s a n d A p p l i c a t i o n s - C I M A ' 2 0 0 1 , B a n g o r ,W a l e s , U K , J u n e 1 9 - 2 2 , 2 0 0 1 , I C S C A c a d e m i c P r e s s , C a n a d a / T h e N e t h e r l a n d s , p p . 4 6 3 - 4 6 9 .
Machine-to-Machine CommunicationMachine-to-Machine Communication
P2P ontology
P2P ontology
Heterogeneous machines can “understand” each other while exchanging data due to shared ontologies
Semantic Web-Supported Sharing and Semantic Web-Supported Sharing and Integration of Web ServicesIntegration of Web Services
Different companies would be able to share and use cooperatively their Web resources and services due to standardized descriptions of their resources.
P2P ontology
P2P ontology
Corporate/Business HubCorporate/Business Hub
Publish own resource descriptions
Advertise own services
Lookup for resources with semantic searchAutomated access to enterprise (or partners’) resources
Hub ontologyand shared domain ontologies
Seamless integration of services
Software and data reuse
Partners / Businesses
What parties can do:What parties achieve:
Ontologies will help to glue such Enterprise-wide / Cooperative Semantic Web of shared resources
Companies would be able to create “Corporate Hubs”, which would be an excellent cooperative business environment for their applications.
Web Services for Smart DevicesWeb Services for Smart Devices
Smart industrial devices can be also Web Service “users”. Their embedded agents are able to monitor the state of appropriate device, to communicate and exchange data with another agents. There is a good reason to launch special Web Services for such smart industrial devices to provide necessary online condition monitoring, diagnostics, maintenance support, etc.
OntoServ.Net: “Semantic Web Enabled Network of Maintenance Services for Smart Devices”, Industrial Ontologies Group, Tekes Project Proposal, March 2003,
Global Network of Maintenance ServicesGlobal Network of Maintenance Services
OntoServ.Net: “Semantic Web Enabled Network of Maintenance Services for Smart Devices”, Industrial Ontologies Group, Tekes Project Proposal, March 2003,
Embedded Maintenance PlatformsEmbedded Maintenance Platforms
Service Agents
Host Agent
Embedded Platform
Based on the online diagnostics, a service agent, selected for the
specific emergency situation, moves to the embedded platform to help the host agent to
manage it and to carry out the predictive
maintenance activities
Maintenance Service
OntoServ.NetOntoServ.Net Challenges Challenges
• New group of Web service users – smart smart industrial devicesindustrial devices.
• InternalInternal (embedded) and externalexternal (Web-based) agent enabled service platformsservice platforms.
• “Mobile Service ComponentMobile Service Component” concept supposes that any service component can move, be executed and learn at any platform from the Service Network, including service requestor side.
• Semantic Peer-to-PeerSemantic Peer-to-Peer concept for service network management assumes ontology-based decentralized service network management.
Agents in Semantic WebAgents in Semantic Web
1. “I feel bad, pressure more than 200,
headache, … Who can advise what to do ? “
4. “Never had such experience. No
idea what to do”
3. “Wait a bit, I will give you some pills”
2. “ I think you should stop drink beer for a while “
Agents in Semantic Web supposed to understand each other because they will share common standard, platform, ontology and language
The Challenge: The Challenge: GGlobal lobal UUnderstanding enderstanding eNNvironmentvironment ( (GUNGUN))
How to make entities from our physical world to understand
each other when necessary ?..
… Its elementary ! But not easy !! Just to make agents from them !!!
GUN ConceptGUN Concept
Entities will interoperate through OntoShells, which are “supplements” of these
entities up to Semantic Web
enabled agents
1. “I feel bad, temperature 40, pain in stomach, … Who can advise what to do ? “
2. “I have some pills for you”
Semantic Web: Before GUNSemantic Web: Before GUN
Semantic Web Resources
Semantic Web Applications
Semantic Web applications “understand”, (re)use, share, integrate, etc. Semantic Web
resources
GUN Concept:GUN Concept: All GUN resources “understand” each otherAll GUN resources “understand” each other
Real World objects
OntoAdapters
Real World Object ++ OntoAdapter +
+ OntoShell == GUN ResourceGUN Resource
GUNGUN
OntoShells
Real World objects of new generation (OntoAdapter inside)
Intelligent Query Routing in P2P Environment
history
Semantically enriched query
package
Adding extra knowledge to query package peers make its routing in the network more intelligent. Adding extra knowledge about
neighbors in a history database of a peer enables intelligent routing.Peers having similar experience can help other peers to find
appropriate service.
EDUTELLA
semantically annotated data repository
semantic query(RQL, RDF-QEL-i )
EDUTELLA project is a multi-staged effort to scope, specify, architect and implement an RDF-based metadata infrastructure forP2P-networks based on the recently announced JXTA framework.
http://edutella.jxta.org/
Interoperability of Heterogeneous Software
Recently in increasing frequency a problem of interaction between heterogeneous software rises. Semantic annotation of exchange data
based on common ontology will enable interoperability and intelligent processes support.
(Semantic)GUNGUN
environment
Java package
Dynamic Link Library
Database server
cgi-script (semantic) OntoAdapter
Industrial Ontologies GroupIndustrial Ontologies GroupFuture Plans:Future Plans:
“Applications of Wireless Semantic WebApplications of Wireless Semantic Web”
Semantically annotated personal dataSemantically annotated personal data
Virtually all resources have to be marked with semantic labels that show explicitly theVirtually all resources have to be marked with semantic labels that show explicitly themeaning of the resource (piece of data, fact, value etc.) It will make possible for user: meaning of the resource (piece of data, fact, value etc.) It will make possible for user:
– To organize own view on data and use it for data management To organize own view on data and use it for data management – To access own and other’s resources with semantic queries using “terms” of own modelTo access own and other’s resources with semantic queries using “terms” of own model– To be able integrate data from other sources To be able integrate data from other sources
(semantics of data is important, data can be converted/translated if needed and appropriate mapping exists)(semantics of data is important, data can be converted/translated if needed and appropriate mapping exists)
Applications will have:Applications will have:– Possibility to discover and operate with user information and preferencesPossibility to discover and operate with user information and preferences– Possibility to share information with applications at other devices and elsewherePossibility to share information with applications at other devices and elsewhere
My data descriptionmodel (ontology)
Commondata semantic descriptions
(ontologies)
My resourcesand their descriptions
Personaldata-view
Applications
mapping between views
Other people’sdata-views
User data becomes available to variety of applications and other people
Semantic Web Semantic Web Inside™Inside™
Commitmentto ontology
Modelling of personal data viewsModelling of personal data views
Simple user data view (as is in most of mobile phones)Simple user data view (as is in most of mobile phones)
Model of user’s data and other resources:- Contacts (phone numbers, names etc.)- Notes (some pieces of text)- Calendar (with some events assigned)
It is rather simple, but a good beginning for own data model creation…..
Data to store in every instance of defined information model
Actually, this model is a simple ontology of “Personal Data” domain.
Using developed standard ontology languagesit will be stored in universal data format.
Building own data model…Building own data model…
added slot (property/field)
inherited slot
Building own data structureBuilding own data structure
added slot (property/field)
inherited slot
Inherited properties
“Relative is a kind of friend”
“Relative is a kind of friend”
Links to otherdata entities
Building own data structureBuilding own data structure
added slot (property/field)
inherited slot
Customized data model:Customized data model:• new kinds of datanew kinds of data• new kinds of representationnew kinds of representation• rules and constraints for data etc.rules and constraints for data etc.• association of data with applicationsassociation of data with applications
Customized data model:Customized data model:• new kinds of datanew kinds of data• new kinds of representationnew kinds of representation• rules and constraints for data etc.rules and constraints for data etc.• association of data with applicationsassociation of data with applications
Using generated interfaceUsing generated interface
Data view is described as an ontology which contains all needed information about data structure. User interface is built dynamically from ontology:• Fields for data• Form layout, types of controls (e.g. picture, checkboxes etc.)• Rules for data that can check some constraints, invoke actions, perform calculations – whatever!
For described data model forms are generated
Access your data quickly and easily…Access your data quickly and easily…
Terziyan’s Contact data
Event data
Possibilities to build flexible, easily customizable data management applications are great.
Just click to open
Every piece of data is somehow described in user’s terms from data-view ontology.Links between data make it easy to find needed information
Customizable personal information Customizable personal information management environmentmanagement environment
Personal data “view”:Personal data “view”:• Development of own view on personal data• Reusing of existing views (join, modify, extend)• Links between personal and some “global” ontology
Sharing of data:Sharing of data:• Applications use data and do it correctly (because of semantics assigned)• Applications can exchange data with external sources• Data can be translated in respect of its semantics
(for localization, between different data views, to fit some requirements etc.)
In such environment even development of own applications/scripts can be possibleIn such environment even development of own applications/scripts can be possible
Ontologies Ontologies andand Semantic Web Semantic Web will enable such kind of applicationswill enable such kind of applications
Easy-to-use, flexible, customizabledata management for users
Repositories of readydata-views
Note: Protégé-2000 ontology development and knowledge acquisition tool was used for demonstration
Enabled collaboration and interoperability
OntoCacheOntoCache
General ontology
Semantic annotations of Web-services (or any other resources) based on shared ontologies enhance much the efficiency of their search/browsing from the PDA. Local ontology adapts permanently to the user preferences.
Personalontology
OntoCacheOntoCache: : benefitsbenefits
Technology that supports future Ubiquitous Semantic Web
Effective filtering of wide variety of Web-resources
Support for semi-natural queries
Context and preferences-based adaptation
Phone calls are also possible between mobile terminal agents. They are performed without human participation in order to exchange local information.
Agent-to-Agent communicationAgent-to-Agent communication
Semantic annotation of the local data enables its intelligent processing by software. Ontologies provide interoperability between heterogeneous peers.
Agent-to-Agent communicationAgent-to-Agent communication
Health
Cooking
Business
?Whatever
semantics enablesintelligent data processing
ontological relationsdefine possible
cooperation betweendomain agents
shared ontologyensures
interoperability
TelemedicineTelemedicine
InIn thethe officeofficeOutsideOutside
FishingFishing
AnywhereAnywhere
At universityAt university
On a beachOn a beach
Health CenterHealth Center
Cases of Cases of
“Human “Human
Maintenance” Maintenance”
ActivitiesActivities
InteractionInteraction
“Recovery” Agents
“Diagnostic” Agents
“Platform Steward”
“WatchDog”“Therapist”
Human and Human and Local Health Maintenance CenterLocal Health Maintenance Center
Remote Health Maintenance CenterRemote Health Maintenance Center
“Recovery” Agents
“Diagnostic” Agents
“Therapist”
“Platform Steward”
Maintenance Crew Service
Health Maintenance Health Maintenance without without barriersbarriers
Anytime and AnywhereAnytime and Anywhere
OntoGamesOntoGames:: New Games GenerationNew Games Generation
CGPCGPPUPPUP
Personal User ProfilePersonal User Profile Common Games ProfileCommon Games Profile
Personal ontologyPersonal ontology General ontologyGeneral ontology
OntoGamesOntoGames: Semantic Games Space: Semantic Games Space
Personal ontologyPersonal ontology General ontologyGeneral ontology
OntoGamesOntoGames: Exit in the Real Life: Exit in the Real Life
Realit
y connec
tion
Realit
y connec
tion
via the g
ame
via the g
ame
Reality connection
Reality connection
via the game
via the game
General ontologyGeneral ontologyPersonal ontologyPersonal ontology
Non Stop Game - Non Stop LifeNon Stop Game - Non Stop Life
OntoGamesOntoGamesCCONNECTING ONNECTING P PEOPLEEOPLE
BANKBANK: Data : Data annotationannotation
In order to make miscellaneous data gathered and used later for some processing,every piece of data needs label assigned, which will denote its semantics in terms ofsome ontology. Software that is developed with support of that ontology can recognize the data and process it correctly in respect to its semantics.
Ontology of gathered data
Web forms and dialogs generated
An
no
tate
d d
ata
(RD
F)
Processing of data by some other semantic-aware applications
BANKBANK: : Customer’s data processingCustomer’s data processing
DataStorage
BankClients
Ontology
BankClients
Input forms
Intelligentontology-based
software
Clients clustering
BANKBANK:: Services annotationServices annotation
Semantics enabled services – Semantics enabled services – easy way to use for customereasy way to use for customer
Semantically Semantically annotated bank annotated bank
servicesservices
I want to …Information filing, Information filing, all documentation all documentation and transactionsand transactions
Less detailed Less detailed informationinformation
Agent-assistant Agent-assistant
Customer Customer
BANKBANK: Loan Borrower annotation: Loan Borrower annotation
Loan borrowersLoan borrowers
Bank - investor Bank - investor
Automated support of:Automated support of:• making decisions about trustingmaking decisions about trusting• prediction of future trends prediction of future trends via semantically annotated loan via semantically annotated loan
borrowers informationborrowers information
Read Our Recent ReportsRead Our Recent Reports
• Semantic Web: The Future Starts TodaySemantic Web: The Future Starts Today– (collection of research papers and presentations of Industrial Ontologies
Group for the Period November 2002-April 2003)
• Semantic Web and Peer-to-Peer: Semantic Web and Peer-to-Peer: Integration and Interoperability in IndustryIntegration and Interoperability in Industry
• Semantic Web Enabled Web Services: Semantic Web Enabled Web Services: State-of-Art and ChallengesState-of-Art and Challenges
• Distributed Mobile Web Services Based on Semantic Web: Distributed Mobile Web Services Based on Semantic Web: Distributed Industrial Product Maintenance SystemDistributed Industrial Product Maintenance System
• Available online in: http://www.cs.jyu.fi/ai/OntoGroup/index.html
Industrial Ontologies GroupIndustrial Ontologies Group
V. Terziyan
A. Zharko
O. Kononenko
O. Khriyenko
Semantic Web: The Future starts todaySemantic Web: The Future starts today
e-Business,net-marketse-Business,net-markets
“Web Of Trust”“Web Of Trust”
Enterprise
Application
Integration
Enterprise
Application
Integration
Interoperability standardsInteroperability standards
Web-servicesWeb-services
Industrial Ontologies Group: Examples Industrial Ontologies Group: Examples of Related Contactsof Related Contacts
24
Participation inOntoWeb Network
The goal of the OntoWeb Network is to bringtogether researchers and industrials comingfrom the research and applications areas,promoting interdisciplinary work andstrengthening the European influence onSemantic Web standardisation efforts such asthose based on RDF and XML. Europe'scultural diversity and multi linguality, togetherwith the strong scientific competenciesexisting in the ontology field, may give Europea unique opportunity to fully exploit ontology-based technology and to play a leading role inthese emerging area.
http://www.ontoweb.org
24
University of Jyvaskylais Member of MeT
MeT is an initiative founded byEricsson, Motorola and Nokiato establish a framework forsecure mobile transactions,ensuring a consistent userexperience independent ofdevice, service and network.
http://www.mobiletransaction.org
25
University of Jyvaskylais Member of WIM
http://www.cs.auc.dk/WIMWireless Information Management (WIM) is aresearch training network involvingresearchers from six universities in Denmark,Finland, Lithuania, Norway, and Sweden.Approximately 30 Ph.D. students and theiradvisors and colleagues take part. WirelessInformation Management encompasses themanagement of information obtained fromsensors as well as the management ofinformation involving mobile objects, both ofwhich types of information concern continuouschange, be it in virtual spaces or physicalspace. These types of data will gainprominence in step with the increasingdeployment of wireless communications andsensor technologies.The project aims to offer training in this area,in which there are significant industrialstrengths and interests in the Nordic region.
1. Aalborg University2. Norwegian University of Science and Technology3. University of Jyvaskyla4. Uppsala University5. Vilnius Gediminas Technical University6. Agder University College
26
IT Faculty (Jyvaskyla) - AI Department (Kharkov):Ukrainian students in Mobile Computing Line
Students Background
-C and JAVA Programming
-Network Management
-Mobile Technologies
-Intelligent Agents Technologies
-Web-content Filtering and Personalization
-Data and Web Mining
-Knowledge Management
-Semantic Web (XML, RDF, RDF Schema,DAML)
-Mobile Commerce Applications
-Object-oriented Design
-Database Management
Special personal abilities
-strong motivation-analytical thinking-adaptivity-self-learning-knowledge acquisition-flexibility-professionalism
http://www.cs.jyu.fi/ai/projects/master01/
Distributed Artificial Intelligence inMobile Environment (2 ov.)
Lecturer: Vagan Terziyan
University of Jyvaskyla, MIT Department, Fall 2001, 2002
Vrije Universiteit Amsterdam, AI Department, Fall 2001
Intelligent Web Applications (2 ov.)
Lecturer: Vagan Terziyan
Vrije Universiteit Amsterdam, AI Department, Fall 2002
Web Content Management (6 ov.)
Lecturer: Vagan Terziyan
Jyvaskyla Polytechnic, Spring 2002
University of Jyvaskyla Experience:University of Jyvaskyla Experience:Examples of Related CoursesExamples of Related Courses
18
Digitaalisen median erityiskysymyksiä (2 ov) seminaarin aihepiiri:
Semanttinen webLecturer: Airi Salminen
University of Jyvaskyla, CS & IS Department, Spring 200218
Structured Electronic Documentation
Lecturer: Matthieu Weber
University of Jyvaskyla, MIT Department, Fall 2001, 2002
Intelligent Information Integrationin Mobile Environment (4 ov.)
Lecturer: Vagan TerziyanUniversity of Jyvaskyla, MIT Department, Spring 2002
Cooperation with American UniversitiesCooperation with American Universities
Ioannis KakadiarisIoannis Kakadiaris Ass. Professor, Department of Computer Science, University of University of HoustonHouston, USA
Ioannis is the founder and Director of Visual Computing Laboratory and the Director of the Division of Bioimaging and Biocomputation at the UH Institute for Digital Informatics and Analysis. He is the recipient of a year 2000 NSF Early Career Development Award.
Cooperation focuses to investigating issues related to management of the Web content which includes human motions as its component, according to the common framework of management multimedia content in the Semantic Web. Possible applications considered:
- Automatic remote camera control (behavior recognition, intentions capture, operator (astronaut) actions control etc.)
- Semantic video transmission (transmit wireless only recognized semantics of motions).
John CannyJohn CannyProfessor, Division of Computer Science, University of University of CaliforniaCalifornia, , BerkeleyBerkeley, USA
John came from MIT in 1987 after his thesis on robot motion planning, which won the ACM dissertation award. He received a Packard Foundation Fellowship and a PYI while at Berkeley. He developed inexpensive, ubiquitous telepresence robots called "PRoPs”...
Cooperation focuses to following subjects:
- Knowledge management of a community of trust;- Collaborative Filtering with Privacy;- Intelligent Integration of Filtering Models;- Adaptive User Interfaces;- Human-Centered Computing;- Online Collaborative learning.
• Developing ontology languages, ontologies, annotation support tools will give you an advance of several years before others can develop the same. Important is that the standards and the applications will depend on you.
• Developing Semantic Web service platforms, agents, applications, based on widespread standards allows to automatically explore rich Web content providing services for millions of customers.
• Annotate your own products and services. This makes your products and services reachable by new generation of semantic search engines and automatically accessed by Web applications, agents and services.
Company Benefits from the Semantic WebCompany Benefits from the Semantic Web
ConclusionConclusion
• Semantic Web is not only a technologytechnology as many used to name it;
• Semantic Web is not only an environmentenvironment as many naming it now;
• Semantic WebSemantic Web it is a new contextcontext within which one should rethink and re-interpret his existing businesses, resources, services, technologies, processes, environments, products etc. to raise them to totally new level of performance…
------------------------------------------Contact: Vagan Terziyan [email protected]://www.cs.jyu.fi/ai/vagan (tel. +358 14 2604618)
“Ask not what the Semantic Web Can do for you, ask what you can do
for the Semantic Web”
Hans-Georg Stork, European Union
http://lsdis.cs.uga.edu/SemNSF