from information to knowledge: some reflections on the origin of the current shifting towards...

From Information to Knowledge: Some Reflections onthe Origin of the Current Shifting Towards KnowledgeProcessing and Further Perspective

Vesna Oluic-VukovicProRes Ltd., Nova cesta 28, 10000 Zagreb, Croatia. E-mail: [email protected]

Although the importance of transforming informationinto knowledge was recognized early, the stronger shifttowards knowledge processing has occurred recently,moving the processes of knowledge gathering, organi-zation, representation, and dissemination into the centerof research attention. In the first part of this article anattempt is made to provide more insight into the reasonsthat prompted the current shift from information toknowledge processing, encompassing both social con-textualization and the recent technological advance.Thereafter, the knowledge production, viewed as five-step processing, is briefly discussed. In the last section,the highly interdisciplinary perspective and the primacyof the user are distinguished as necessary prerequisitesfor (a) solving the basic set of problems addressed byknowledge processing, and (b) improving the user–sys-tem interaction.

Introduction

Although the information science is not about technol-ogy, the information system and technology impact infor-mation sciencesubstantively, stimulating (forcing) and con-straining its evolution. In effect, so far the changing empha-ses in information science studies were mainly influencedby increasing use of technology (i.e., in database design,construction, and retrieval), and there isno reason to believethat this interaction or mutual reinforcement would notcontinue. Namely, if the recent literature on informationscience is considered, it becomes evident that these ideas,practices, and researches that are gathering around notionssuch as the information highway, multimediaand hypertext,Internet and Intranet, World Wide Web, digital libraries,electronic publishing, artificial intelligence etc., as a con-temporary culmination of the half century development ofinformation and communication technologies, attract greatinterest. Moreover, the more sophisticated approaches rec-ognized that the current movement represents, in fact, nar-rowing, focusing, and institutionalizing some of previously

quite general concepts and ideas well known to informationscience (Bush, 1945; Otlet, 1934; Wells, 1938), and theirtransformation into a viable scheme.

Consequently, in anticipating the future progress of in-formation science the implications of advanced informationtechnology, or in Wersig’s (1993) sense, “the postmodernconditions of informatization,” cannot be avoided irrespec-tive of whether the further development is considered fromthe narrow aspect, i.e., by strict adherence to acertain topicor theme, or from the broader point of view. Although thereis ageneral consensus that today’s technology significantlyimproved the processing of information, the opinions re-garding the future impact substantially differ. They rangefrom the more or less utopian expectations that technology,as a driving force, wil l be able to solve all information-related problems, through thoseemphasizing dystopian pos-sibilities, to more realistic opinions holding that technologydefines available processes, i.e., by improving interactivecapabilities for handling and integrating information, re-gardless of medium or format, but not purposes. While theformer opinions, governed mostly by technological deter-minism, consider technology as an autonomous force withthe power to achieve aunilateral impact (positive or nega-tive), the latter proceed from the complex mutual influencesand interactions between technology and socially consti-tuted needs, emphasizing that technology is ameans, not anend. Technology can yield useful insight and clues, butshould not be expected to produce definitive answers.

Although the inherently pluridisciplinary field of infor-mation science on one side, and the increasingly electroni-cally mediated environment on the other, form a broadcontext for various concepts or different visions of thefuture, in what follows the focus wil l be on knowledgeprocessing, a rapidly emerging field that attracted aremark-able level of interest during the last decade.

The Rationale

In an attempt to anticipate the possible area of futureinterest, and to avoid vagueness in approaching it, the© 2001 John Wiley & Sons, Inc. ●

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similarity and the difference between two formulations,each reflecting the state and the tendencies in informationscience in two different periods of time, were chosen as avaluable starting point. The first one is the well-knowndefinition of Borko (1968), introduced with the aim to givemore sense to cacophonous discourse surrounding themeaning of information science at that time:

Information science is that discipline that investigates theproperties and behavior of information, the forces governingthe flow of information, and the means of processing infor-mation for optimum accessibility and usability. It is con-cerned with that body of knowledge relating to the origina-tion, collection, organization, storage, retrieval, interpreta-tion, transmission, transformation, and utilization ofinformation. It has both a pure science component, whichinquires into the subject without regard to its application,and an applied science component, which develops servicesand products.

Thirty years later, in the clearly and elegantly formulatedstatement given in the announcement of the ASIS 1999Annual Conference, the following is stressed:

Our ability to transform data into information, and to trans-form information into knowledge that can be shared, canchange the face of work, education and life. We haveincreasing capacity to generate or gather, model, representand retrieve more complex, crossdisciplinary and multifor-mat data and ideas from new sources and at varying scales.The transformational power of information can only becapitalized upon through knowledge acquisition, classifica-tion, utilization and dissemination research, tools and tech-niques.

By comparing the above two formulations, it can beeasily seen that in the second formulation the emphasis isgiven on knowledge, not on information as was the case inthe first. Although information still constitutes the essentialpart, the ultimate intellectual problem is not the effectivemeans of information provision, but production and use(consumption) of knowledge. Relative to the notion ofknowledge in this context, the following distinction is worthmentioning. While so far in information technology, eitherin the context of business enterprise or the personal com-puter user, the knowledge tended to connote possession ofexperienced “know-how” as well as possession of factualinformation, or ability to locate it quickly, a new technologyattempts to develop knowledge analyzing the data (capturedfrom diverse sources) for relationships that have not beendiscovered previously, including associations, sequences,classification, clustering, and forecasting.

Because knowledge involves the synthesis of informa-tion (data), what people do with facts and interpretationsthey acquire, it is not enough to implement systems thataccelerate and simplify data processing, but to design sys-tems shaped much more in relation to how human mindsand information needs actually function (see Bush, 1945).

To address the boundary between knowledge and data pro-cessing, Rappaport (1997) used a cognitive approach bycontrast with an operational definition:

In the latter case, clustering algorithms or other types ofanalytic method may yield a richer picture of a set of data.Ultimately though, the “cluster” is another, more abstractpiece of data, a type of compressed or distilled view, moreuseful but yet to be integrated into a broader cultural frame.Consider on the other hand that to reach the minimal “the-ory of mind,” one must not only build such abstractions butalso make use of “higher order knowledge.” First-orderknowledge is purely factual while second-order deals withbeliefs about others’ first-order knowledge and so on. Suchknowledge would constitute an important component of the“culture” of the entity considered, individual, group orsocieties.

The idea of generating useful knowledge from a hugeamount of data is not new, and its origin can be traced backto the late 1950s, when it was primarily and mainly the issueattracting artificial intelligence researches. However, thestrong resurgence of interest occurring in the last decade hasshifted the knowledge processing into the center of researchattention of a broader scientific community.

In what follows, an attempt is made to provide moreinsight into the reasons that prompted the current shiftingfrom information to knowledge processing. Thereafter, thefive-process view of knowledge processing is briefly dis-cussed. In the last section, the interdisciplinary perspectiveand the primacy of the user are distinguished as necessaryprerequisites for the successful implementation of knowl-edge systems.

The Shift Toward Knowledge Processing—Mutual Causality

It was not our intention to present a complex schemewith all its interactive details, but rather to provide a min-imum meaningful context encompassing both social contex-tualization and technological advance, from which the cur-rent needs for knowledge production are assumed to arise.By keeping the focus on that very general level, the follow-ing pattern emerges interlinkig two broad environments,i.e., the information production and the information useenvironments, both affected by ongoing intervention of newtechnology (Fig. 1).

As follows from Figure 1, the initial impetus or stimulusis supposed to come about through the rapid development ofinformation technology (i.e., through its incentive as well asenabling effects). Throughout this article the term informa-tion technology is used as a generic name comprehendingboth information and communication technologies, or moreprecisely, technologies and applications that combine thedata processing and storage power of computers with thedistance-transmission capabilities of telecommunications(Child, 1987).

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The impact of information technology is evident (directlyor indirectly) through three types of dynamic interplay. Thefirst type isinformation technologyN information produc-tion. The second type isinformation technologyf infor-mation use environment. The feedback effect of the first twointerplays is outlined in the third type,information produc-tion f information use environmentf information tech-nology. Under the broad term information use environmentintroduced by Taylor (1991), the socially constituted infor-mation needs and demands arising from the increasingnumber of essential economic, cultural, political, and socialrelations mediated by information technology, are assumed.Or to put it in Taylor’s term, it refers to the set of thoseelements that affect the flow and use of information into,within, and out of any defined entity, and determine thecriteria by which the values of information will be judged.

Each of the above given interplays will be shortly dis-cussed below.

(1) Information technologyf Information production.There is growing evidence that technology for produc-tion, storage, and distribution of massive quantities ofelectronic information has progressed enormously. Thewidespread availability of global information systemslike the Internet, combined with the development ofeasy to use graphical browsing interfaces has led to anexplosion in the amount of information being added.The publicly indexable World Wide Web, distributed,dynamic, and rapidly growing information resource,contains an estimated 800 million pages, encompassingabout 6 terabytes of text data (Lawrence & Giles, 1999),and it is expecting to approach one billion documentsby the year 2000. A few dozen of bibliographic data-bases have rapidly grown into several thousands data-bases containing everything from full-text documents tomovie clips. Due to rapid proliferation of large-scaledigital libraries, database management systems, and In-ternet servers, the amount of information easily acces-sible from the desktop has dramatically increased byseveral orders of magnitude in the last few years, andshows no signs of abating. The adverse side effect of theenormous growth of the amount of information is in-formation overload followed by: (a) inability to inter-pret and digest these data as readily as they are accu-mulated, and consequently (b) an increase in uncer-

tainty and complexity. Although there are someopinions that the talk about information overload is a bitmisleading, because it is the efficiency of accessinginformation that has greatly improved rather than theamount of information, it is quite certain that the effectsof new technology on quantity and quality of informa-tion are quite different. Although the rate of producinginformation has increased significantly, the time re-quired to evaluate it improves less rapidly. Conse-quently, the users (particularly the users of the Web),are being confronted with the difficulties in locating theresources that are both high quality and relevant to theirinformation needs. Formal searchers, however, preferinformation from sources that are perceived to beknowledgeable or from information services that makeefforts to ensure data quality and accuracy. Understand-ing user behavior and incorporating that understandingin system design therefore represents serious chal-lenges. While technology influences the amount andtype of information available, it must also providemeans to make effective use of this information, i.e., toprovide efficient, adaptive, and intelligent access with-out creating an information overload on the users. Al-though over the past few years, the primary interface ofthe Web has evolved from browsing to searching, thecommercial technology of searching large collectionshas remained largely unchanged (Chen, 2000).

(2) Information technologyf Information use environ-ment. In anticipating the social consequences of thecurrent moving towards worldwide networking thatbrings information into a wide range of institutionalsettings, Dyson, Gilde, Keyworth, and Toffler (1996), inthe preface of a rather provocative article, stressed thefollowing: “As humankind explores this new ‘electronicfrontier’ of knowledge, it must confront again the mostprofound questions of how to organize itself for thecommon good. The meaning of freedom, structures ofself-government, definition of property, nature of com-petition, conditions for cooperation, sense of commu-nity and nature of progress will each be redefined forthe Knowledge Age just as they were redefined for anew age of industry some 250 years ago.” This is,however, one of the possible views, because the opin-ions regarding the impact of information technology onsociety in general and organizational structuring in par-ticular, differ significantly (Drucker, 1988; Huber,1990; Kling & Zmuidzinas, 1994; Orlikowsky, 1992).Although there is a general agreement that informationtechnology generates opportunities for new methods oforganizing people, work, material, resources, and time,some authors emphasize that whether these opportuni-ties will be perceived and used depends on a particularorganizational context (see Kling & Zmuidzinas, 1994).An extensive corpus of literature presently covers theimpact of today’s technology on social and organiza-tional change, showing how and when technology altersthe traditional assumptions upon which organizationsare based (summary in Travica, 1998). Apart from thedifferences in the ways and degrees of altering thecontent, quality and the organization of work, it be-comes more and more evident that a rapid developmentof information technology was paralleled by: (a) orga-

FIG. 1. Information processing, information use environment and infor-mation technology—mutual interactions.

56 JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY—January 1, 2001

nizational changes—organizational missions, goals,and strategies were redesigned, typically emphasizingthe increase in competitiveness and value for custom-ers; and (b) new and emergent fields like distanceeducation, knowledge management, infrastructure plan-ning, business planning, e-commerce. This transitiontowards higher quality production, and a more efficientorganization based primarily on information and knowl-edge, is followed by rearrangement of priorities andworkloads change, affecting the information behaviorof the user (Rosenbaum, 1997), which in turn, posed thenew challenges for information technology, as will beshortly described below.

(3) Information productionf Information use environ-mentf Information technology.Among the reasonsthat pursued the knowledge facilitation initiatives of theUnited Technologies Corporation Information Net-work, Steele (1998) pointed out the persisting problembetween information resources and problem solving,showing explicitly how information resources both en-able and constrain the information behavior of the user:“Some of our customers were asking for new services.Instead of lacking information, they cited data overloadas a major problem. They had difficulty making deci-sions and advancing projects because they had to wadethrough too much data.” What most users want is qual-ity output, not quantity. The difficulty of discerning thevalue in information prevents many companies fromfully capitalizing on the wealth of data at their disposal.It follows, therefore, that the urgent needs to acceleratediscovery of knowledge in large databases are createdby: (a) increasing amount and complexity of data, andinability of the available search engines to provideinformation that satisfy user’s requirements; and (b) theemergence of knowledge-based societies, paralleled byreplacement of capital and labor-intensive organizationswith knowledge-intensive organizations, where thequality and availability of intellectual capital becomecritical success factors, allowing business to make pro-active, knowledge-driven decision (Cronin, 1991). Ev-idently, the knowledge demands arise as a feedback,pushing the modern information technology toward cre-ating a new generation of tools and techniques forautomated and intelligent processing of large quantitiesof data, identifying the most significant and meaningfulpatterns, and presenting these as knowledge appropriatefor achieving the user’s goals.

Although simplified, the above consideration allows usto conclude the following:

(1) The current shift toward knowledge processing isdriven by a mix of daunting practical needs set by thesociety, and the recent technological advances thatmake the concept of generating useful knowledge froma huge amount of data more feasible.

(2) The information technology is unlikely to continue onits own, but as a result of interaction with social contextthat affects the patterns of information behavior. Tech-nology drives society, and vice versa, the application oftechnology both adapts technology in distinctive ways

and manifests requirements that can stimulate furtherdevelopment in novel directions. According to Taylor(1991), the problems generated within information useenvironment are dynamic, and the user’s understandingof problems changes over time “in response to newinformation and in relation to the actor’s position andperception.” The multi- and mutually causal relation-ships between the information use environment andinformation technology find convincing evidence in thestudies performed within the field of social informatics(see Kling, 1997).

(3) To bridge the gaps in relation to various knowledgerequirements set by society, it is not enough to focusexclusively on improving or developing new technolog-ical solutions, but also on understanding (a) the contextsin which the knowledge demands arise, and (b) theunderlying concerns driving users today.

In the next section a wide range of activities involved inthe knowledge processing is briefly outlined. The aim is todemonstrate the complexity of the problem that involvesmany stages and addresses many different needs.

Knowledge Processing—The Major Processes

Table 1 presents a five-process view of knowledge pro-cessing, including some of the main activities associatedwith each process.

Because the present work is not meant to be a compre-hensive review, nor to summarize the latest development(this requires much more than a few pages within thecontext of this work), the major processes will be describedin a very general manner, and with the aim to provide moreinsight into the type of the activities involved in the pathfrom data to knowledge.

(1) Knowledge discovery—Involves a multistep process offinding and defining significant patterns in data exam-ined by software agents (see Matheus, Chan & Pia-tetsky-Shapiro, 1993). A subset of steps that allows theagent to examine the database is called data mining, i.e.,the process of extracting valid, previously unknown,comprehensible, and actionable information from largedatabases (Fayyad, 1996). According to Raghavan,Deogun, & Sever (1998), a successful knowledge dis-covery depends on choosing the appropriate model,realizing the assumptions inherent in the model, andusing a proper representational form. Chen and Ng(1995) stressed that the knowledge discovery has alsomade possible development of large knowledge basesas “online repositories of high-level, abstract humanknowledge represented in terms of heuristic, inferenc-ing rules, problem-solving strategies, networks of inter-related concepts (concept space), and so on.” Whilemost work on knowledge discovery has been concernedwith structured databases, there has been little work onhandling the huge amount of information that is avail-able only in unstructured textual form, although for thepast several years, the full-text segment has been the


fastest growing section of the commercial computerizeddatabase market.

(2) Knowledge organization—Comprises cataloging, in-dexing, filtering mechanisms, clustering techniques,and other means for: (a) placing significant amounts ofdata into a comprehensible number of categories, (b)enabling identification of relevant concepts (knowl-edge) embedded in the documents efficiently and effort-lessly, and (c) visualization of underlying structure forexplanatory purpose. It comprises the activities thatenhance identification, location, retrieval, and manipu-lation using metadata schemes and tools, as well astechniques that enhance the processing and retrievalcapabilities such as latent semantic indexing, symboliclearning algorithms for object classification, geneticalgorithms-based approach for indexing and clustering,etc. Metadata schemes and ongoing efforts of establish-ing metada standards are of utmost importance, partic-ularly in view of the existing problems with current,automatically generated index databases (Desai, 1997).

(3) Knowledge refining—Includes processes and toolsbased on the concepts of content analysis serving toimprove knowledge attributes, i.e., to make it moreconcise, comprehensive, and usable.

(4) Knowledge representation—Usually implies represen-tation in the form of rules—rules indicating the degreeof association between two attributes, rules mappingdata into several predefined classes, rules that identify afinite set of categories or clusters to describe data(Raghavan et al., 1998). To avoid the dichotomy be-tween reasoning and representation, the emphasis in a

knowledge representation system is given on flexibilityof representation, allowing the user to decide if thesystem will basically operate as a theorem prover, aframe-like system, or an associative network. Theknowledge representation such as semantic net, frame,decision trees and logic, grounded on cognitive re-search, are often considered more natural and under-standable for users than statistical formulas or neuralnets (Chen, 1995).

(5) Knowledge dissemination—Involves all the processes,channels, and formats necessary to communicate theknowledge. To speed the knowledge dissemination andfacilitate its availability, access, and ease-of-use, a mixof dissemination channels and formats are designed,while a multitude of communication channels haveevolved to communicate different type of information,including recorded information, Web pages, video, andart objects.

The ultimate goal of all these processes, activities, andtools is to provide knowledge consistent with user’s needs,that is, to provide material that is comprehensible; thatfurnishes information beyond that embodied by the queryitself, to give the user insight into the “big picture”; toderive problem-oriented answers from vast amounts of datathrough statistical analysis, machine learning, and auto-mated reasoning, and to enable access and easy of use. Allthis requires increasingly sophisticated methods for dataextraction, compilation, and presentation.

TABLE 1. Knowledge processing: Major processes and activities.

Knowledge processing

Major process Includes these activities

GatheringKnowledge discovery, capture and creation Data mining; text mining; information extraction

Pulling information from various sources (real-life databases, domain-specific knowledgesources, data dictionaries, etc.)

OrganizingKnowledge classification and structuring Cataloging

IndexingClustering and classificationFilteringLinking

RefiningKnowledge content improvement Contextualizing

CollaboratingCompactingProjectingMining

RepresentingKnowledge representation schemes Semantic networks

FrameDecision treesPredicate logic

DisseminatingKnowledge communication through a mix Flow—Communication

of dissemination channels and formats Sharing—PublishingPush–Push versus pull


Future Outlook

Interdisciplinary Perspective

The processes of knowledge gathering, organization, re-fining, representation, and dissemination, followed by agrowing need for a new generation of tools and techniquesfor automated and intelligent database analysis, constitutean important and active area of research that will arouse aneven greater interest in near future not only for artificialintelligence, computer science, and other functional disci-plines, but also for information science because, as arguedby Saracˇevic (1999):

These areas have a significant informational component thatis associated with information representation, its intellectualorganization, and linkages; meta-information, informationseeking, searching, retrieving, and filtering; use, quality,value, and impact of information; and the like—all tradi-tionally addressed in information science.

Thus, the challenge for information science is to furtherexplore the problems that have always been fundamental toit, as well as to make more transparent the vast body ofknowledge developed so far. Namely, many problems iden-tified within the context of knowledge organization, re-trieval, and dissemination, are not new to information sci-ence. On an example of classification, which has beenlargely ignored by the text retrieval community but nowreceiving attention within the field of artificial intelligence,natural language processing, and software engineering un-der the term ontology, Soergel (1999) illustrated the lack ofcommunication between scientific community, “resulting inconsiderable reinvention and less than optimal products.”Bates (1999) stressed the same problems related to catalog-ing. In Morris (1994), the issue of question negotiation,attracting considerable interest in many different fields, hasbeen used for the same purpose, i.e., to show that differentfields have developed theories and approaches in isolationfrom each other. Yet another example, though not directlyrelated to this topic, arises from the recent paper of Huber-man and Adamic´ (1999). By examining the evolutionarydynamics of the World Wide Web, they have found thatdistribution of pages on Web servers is extremely skewed,following the universal power law. However interesting andimportant the obtained result is, its interpretation is over-simplified, and without any reference to a large body ofinformation science literature dealing with this topic, knownunder the common name informetric distributions (seeOluic-Vukovic, 1997).

Although the interests and agendas of information andcomputer scientists, as well as the scientists from othercontributing fields diverge, the appropriate solution of thebasic set of problems addressed by the current shift towardknowledge processing on one side, and the user’s demandson the other, require a highly integrated approach. Despitethe fact that many specialized, multiple-disciplined, intelli-gent analysis techniques for creating, discovering, and ap-

plying knowledge have advanced significantly, and thepower gained from applying knowledge base technology ismuch greater than in the past, it becomes more and moreevident that production of knowledge appropriately struc-tured and represented needs to be studied under a highlyinterdisciplinary perspective. The recognition that the com-plex demands of knowledge creation, diffusion, and utiliza-tion cannot be resolved with approaches and constructsfrom any single discipline, coincides according to Guarino(1995), with shifting of focus from knowledge form andrepresentation (as was the common practice in artificialintelligence community) to knowledge content. This shift-ing was paralleled by the realization that apart from thetheories drawing on the principle of computer science andartificial intelligence, the contribution of logic, linguistic,and semantic as well as the insights of psychology andsocial phenomena, are necessary to: (a) provide a betterframework for meaningful progress, and (b) constrain thepossibility to state something that is reasonable for thesystem but not for the users. The latter fact brings us quitenear to the problems of user and user–system interaction,addressed by both information as well as computer science,which deserves to be treated separately.

The Primacy of the User

By speaking about two ingredients of success, knowl-edgeable systems, and knowledgeable users working to-gether, Soergel (1998) in “An Information Science Mani-festo,” emphasized the following:

At the heart of information science are the twin concerns ofunderstanding users in their quest for meaning and problemsolutions and presenting knowledge structures that supportthe construction of meaning and the solutions of prob-lems. . . . User models and knowledge representation, likebinary star, revolve around each other and depend on eachother. To know what is important in knowledge represen-tation, we need to know user requirements and user thoughtpatterns.

The exploration of various contexts of user informationbehavior, and the processes of information seeking, search-ing, using and evaluating were traditionally the concern ofinformation science, but also of social science and otherrelated disciplines. By focusing on the Dervin’s, Belkin’s,Taylor’s, and Kuhlthau’s attempts to expand understandingof the user’s information needs, Morris (1994) provided anexcellent, analytical view of user-oriented modeling ap-proaches, offered as an alternative to system-oriented ap-proaches. More recently, Savage-Knepshield and Belkin(1999) presented a comprehensive overview of the ways inwhich user–system interaction in interactive informationalretrieval systems has been treated over the years. Althoughthe investigations of user behavior constitute a substantialbody of literature, they have had little effect on the system


design and operation until recently. According to Rosen-baum (1996):

One reason for this situation is that system- and user-basedapproaches have been working at a disadvantage becauseeach is saddled with an inability to account for the basic andnecessary concept central to other; system-based or struc-tural approaches cannot adequately account for action anduser-based approaches cannot account for structure.

As a result of this, and the prevalence of the system-oriented approaches, the user is mostly considered as pas-sive receiver of information, while information is beingsought as stable, possessed of inherently meaningful con-tent, definable and measurable. This paradigm is shiftedaway by the recognition that the information uses are ac-tions and interactions in which individuals construct senseand resolve problems as they move through situations (Bel-kin, 1980; Belkin, Oddy, & Brook, 1982a, 1982b; Dervin &Nilan, 1986; Harter, 1992; Taylor, 1991). Consequently, theuser becomes the center (focus) of an active, ongoing pro-cess of change, while information is no longer seen as anobjective and stable entity but as subjective, holistic, andcognitive (Dervin & Nilan, 1986).

The reasons leading to the recent, more widely expressedinterest for the characteristics, motives, actions, and beliefsthat users bring to their interaction with system, can beattributed to: (1) the electronic information explosion thathas increased access to information, so that more and moreindividuals begin to use information retrieval systems bythemselves and become end users; and (2) the rapidlyemerging field of knowledge management, much broaderthan information management, that requires understandingnot just of data, but of communicating and using these data.Consequently, the knowledge about user information be-havior becomes important for both (a) system designers tomore effectively design systems that are flexible and ac-commodate for differences among users through interactionand the user interface, and (b) services to facilitate theproblem solving, which, by contrast of providing access toinformation, implies a more direct involvement in a processof understanding the user needs (Morris, 1994).

All this calls into question the one-way directionality ofmany previous approaches and paradigms, showing explic-itly that successful implementation of systems requires botha technical component, concerned with the design and useof systems, and a social component, concerned with under-standing how users seek, obtain, evaluate, use, and catego-rize information. As it was stressed by Saracˇevic (1999):

The issue is how to deliver and incorporate the desireddesign features that will improve systems orientations to-ward users, integrate them with systems features, and useadvantages provided by both, humans and technology. . . .The success or failure of any interactive system and tech-nology is contingent on the extent to which user issues, thehuman factors, are addressed right from the beginning to thevery end, right from theory, conceptualization, and design

process on to development, evaluation, and to provision ofservices.

Reflecting on the user–system interaction in general, andthe more recent problems of the forms of representation orknowledge-mapping in particular, Kling (1997) commentsthat the “older models of human–computer interaction didnot model the kinds of scope and scale found in today’shigh-complexity systems,” since:

The goals of a human centered system are not fixed onceand for all, and then good for all contexts. People who usesystems must be able to help define what they need systemsto do (usually); it certainly means not just testing designwhen one is well down the design path, after it is too late forgood user feedback. In this, we see a desirable shift frompassive users of systems to more active participants insystems at all developmental phases.

These, and many others similar opinions, mark morethan just a return of attention to the role of the user. Theypoint explicitly on the necessity of considering the knowl-edge (content and knowledge functions), technology, andusers as single entity that will shape the future. The increas-ing interest for reasoning and the users’ mental models,followed by (a) the recent technological advances in track-ing what user actually do when searching, and (b) theemergence of new equipment and techniques to supportusers at the interface level (i.e., visualization techniques,natural language interaction), give certain confidence tosuch expectation.

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