From: FLAIRS-01 Proceedings. Copyright © 2001, AAAI (www.aaai.org). All rights reserved. Knowledge Managementand Case-Based Reasoning: a Perfect Match? lan Watson AI-CBR Department of ComputerScience University of Auckland Auckland ian@ai-cbr.org Abstract This paper demonstratesthat case-basedreasoningis ideally suited to the creation of knowledge management systems.Thisis becauseof the close matchbetweenthe activities of the CBR-eycle and the requirementsof a knowledge management system. The nature of kalowledge withinan organisationis briefly discussed and the paper illustrates the dynamicrelationship betweendata, informationand knowledge,showinghow case-basedreasoningcan be usedto help the manage the acquisition and reuse ofknowledge. 1 Introduction The function of knowledgemanagement(KM)is to allow an organisationto leveragethe informationresourcesit has and to support purposefulactivity with positive definable outcomes (Checkland & Scholes 1990). Knowledgeand consequently its management is currently being touted as the basis of future economiccompetitiveness,for example: In the information age knowledge, rather than physical assets or resources is the key to competitiveness... Whatis newabout attitudes to knowledgetoday is the recognition of the need to harness, manageand use it like any other asset. This raises issues not only of appropriate processes and systems, but also how to account for knowledgein the balance sheet (Moran,1999). Entrepreneursare no longer seen as the ownersof capital, but rather as individuals whoexpress their tacit knowledge by "knowing how to do things" (Casson, 1997). Boisot (1998) sees information as the key organizing principle the organization; "information organizesmatter." The introduction of information technology on a wide scale in the last thirty years has madethe capturing and distribution of knowledgewidespread, and brought to the forefront the issue of the management of knowledgeassets (Davenport 1997). The "knowledgemanagement"function is spreading throughoutthe organisation, from marketing, information management systems and humanresources. With knowledgenowbeing viewed as a significant asset the creation and sharing of knowledge has become an important factor within and betweenorganizations. Boisot (1998) refers to the "paradox of value" whenconsidering the nature of knowledge,in particular its intangibility and inappropriateness as an asset and also the difficulty of assessing and protecting its value (Priest 1994). Copyright ©2001,A,~I. All rights reserved. 11e FLAIRS-2001 facihtatesthe I,°,ormat,onl I .ow’ ",ol creation of m~ Figure 1. The DynamicRelationship between Information and Knowledge If, as this paper postulates, CBR is of significant value in KMwe first need to consider the nature of knowledge) 2 A Knowledge Framework A commonapproach to considering knowledge oRen highlights its relationship to information in terms of difference. This perceiveddistinction betweeninformation and knowledgeis not helpful and has led to the current confused preoccupation in the management literature with what is conceived of as a clear distinction between "knowledge managementand information management. Indeed, it is commonto see document management systems branded as KMsystems. Although the relationship between information and knowledgemaybe been seen as "closely associated," it should be more appropriately seen in terms of a "dynamic and interactive relationship." Informationfacilitates the development of knowledge, which creates more information that deepens knowledge, ad infinitum. For example, Nonakaand Takeuchi(1995) stated: Information provides a new point of view for interpreting events or objects, whichmakesvisible previously invisible meaningsor sheds light on unexpected connections. Thus information is a necessary mediumor material for eliciting and constructing knowledge (Nonaka & Takeuchi, 1995). Since this paper is delivered to a CBRaudience I am assumingfamiliarity with the basic processes of CBR.An introduction to CBRcan be found in Watson(1997). fi Perceptual andconceptual filters ................................... p, ! : p’, Event (experientialdatasource) Agent (tacit knowledae source) I Information Data -~- Feedback ....................... J Source:Adaptedfrom Boisot (1998) Figure2. Data,InformationandKnowledge (Boisot, 1998) Whilst Polyani (1967) and Choo(1998) have viewed dynamicinteractive relationship as part of the process of knowingwhichfacilitates the capacity to act in context. The dynamicnature of this relationship is illustrated below in Figure1. Similarly, to look at information purely in terms of the degree to which it has been processed, i.e., the data, information, knowledge hierarchy (Davenport 1997; Checkland & Howell 1998) oversimplifies the complex relationship betweenthe three intangibles. Stewart (1997) notes: The idea that knowledgecan be slotted into a datawisdomhierarchy is bogus, for the simple reason that one man’s knowledge is another man’s data (Stewart 1997). The categories defined by Boisot and their interactions maybe seen in Figure 2. It is important to note the feedback element within this figure that illustrates the dynamicand interactive relationship of information and knowledgeas a positive feedbackloop. Datais discriminationbetweenstates - black, white, heavy, light, dark, etc. - that mayor maynot conveyinformation to an agent. Whetherit does or not dependson the agents prior stock of knowledge.For example,the states of nature indicated by red, amberand green traffic lights maynot be seen as informative to a bushmanof the Kalahari. Yet, they mayperceivecertain patterns in the soil as indicative of the presenceof lions nearby. Thus, whereasdata can be characterised as a property of things, knowledgeis a property of agents predisposing themto act in particular circumstances.Informationis that subset of the data residing in things that activates an agent - it is filtered fromthe data by the agent’s perceptual or conceptualapparatus (Boisot, 1998). This has a direct echo in AlanNewell’sprinciple of rationality: "If an agent has knowledgethat one of its actions will lead to one of its goals, then the agent will select that action" (Newell1982) 3 CBR is a methodology for KM What then are the requirements of a KMsystem? At a recent workshopheld at CambridgeUniversity a group of people active in KMand AI identified the mainactivities needed by a KMsystem (Watson 2000). These were mapped to AI methods or techniques. The main KM activities were identified as the: acquisition, analysis, preservation and use of knowledge.This section will show howCBRcan meet each of these requirements. I argued in 1999 that CBRis not’an AI technology or technique like logic programming,rule-based reasoning or neural computing. Instead, CBRis a methodology for problem solving (Watson 1999). Checkland & Scholes (1990) define a methodologyas: "...an organised set of principles which guide action in trying to "manage" (in the broad sense) real-world problem situations" (Checkland&Scholes 1990) Nowconsider the classic definition of CBR: ".4 case-based reasoner solves problemsby using or adapting solutions to old problems."(Reisbeck & Schank 1989) This definition tells us "what"a case-based reasoner does and not "how"it does what it does. It is in Checkland’s sense an organised set of principles. The set of CBR principles are morefully defined as a cycle comprisingsix activities 2 called the CBR-cycle as shownin Figure 4. 2 Note that the original CBR-cycleof Aamodt& Plasa (1994) comprisedonly four activities. CASEBASEDREASONING 119 o /-----7 i new problem i ~,~ J ’ ? i /---7, REwsE ....... 7/’REwEw / /" e new case new solution Figure 3. The CBR-cycle This cycle comprisessix activities (the six-REs): 1. Retrieve similar cases to the problemdescription 2. Reusea solution suggestedby a similar case 3. Reviseor adapt that solution to better fit the new problemif necessary 4. Reviewthe new problem-solution pair to see if they are worthretaining as a newcase 5. Retainthe newsolution as indicated by step 4. 6. Refine the case-base index and feature weights as necessary. The six-REs of the CBRcycle can be mapped to the activities required by a knowledgemanagementsystem. Let us revisit Figure 2 and superimposethe steps of the CBR-cycleupon it. Nowwe can easily see that the event is the episodic data that comprisesa case. The case is retained in a case-base but typically undergoessomepre-processing and filtering (indexing and feature weighting in CBRterminology). The agent or the reasoner retrieves the case to solve some problemand attempts to reuse it. This mayresult in some revision or adaptation of the case’s solution resulting in a newepisodic data and hence a newcase being created. The newease is reviewed(involving comparingit against cases already retained in the case-base) and if it is judgeduseful retained. In addition, the use of the case (successfully or otherwise) mayresult in the case-base’s indexing scheme being or the feature weights refined. This completes the knowledge management cycle indicated on Boisot’s original diagram. refine Perceptual andconceptual filters ~ retention Event (experiential data source) 3ase-Base retrieval~ Agent (tacit knowledgesource) Data revision reuse~ Figure 4. The CBR-cyclesupporting KnowledgeManagement 120 FLAIRS-2001 4 Multiple Knowledge Sources CBRsystems have traditionally been built around single case-bases designed to solve a particular problem(Watson 1997). However, KMsystems may draw upon multiple heterogeneous knowledgesources, just as you might use several sources whenresearching a paper. Conventional CBRsystems that use a proprietary case-base format cannot integrate knowledgeresources and therefore may not support a user with complex knowledgeneeds. Note that creating multiple case-bases each searched by the sameinterface is onlya partial solution to this problem. Brown et al., (1995) proposed a solution whereby knowledge could be drawn from multiple distributed heterogeneous sources and temporarily held in "virtual cases." This would still require someone(probably knowledgeengineer) to design the structure of the virtual cases in advance. The content of the virtual case-base wouldbe obtained on demand.This is again only a partial solution though it does have merit were real-time dynamic data is usedin cases. Dubitskyet al., (1999) describe the architecture of a CBRwarehousethat could contain multiple heterogeneouscasebases to solve this KMproblem.Their systemrelies on the use of an ontology to overcomethe semantic differences betweencase-bases. Thus, allowing a single CBRretrieval systemto retrieve cases fromdifferent knowledgesources. eGain3 (www.egain.com)have developed a product called eGain KnowledgeGatewaythat provides seamless access to multiple knowledgesources through a single interface (eGain, 2001). This system uses a core CBRcomponent retrieve solutions based upona conversational query from a user. Knowledgecan be retrieved from Lotus Notes databases, Microsoft Office documents, HTML and PDF files, e-mails as well as specifically authoredcase-bases. 5 Conclusion CBRtherefore closely matchesKM’srequirements for the acquisition (revision and retention), analysis (refinement), preservation (retention) and use of knowledge(retrieval, reuse and revision). Dubitskyet ai. (1999) refer to this KM/CBR synergy and it explains why CBRhas been used successfully in manyKMsystems (Aha et al., 1999). However,it is not a one-sided relationship. KMhas as much to offer the success of CBRas vice versa. In particular KMresearchers and practitioners have recognise the importanceof organisational issues in the success of a KMsystem and there is muchthat CBRpractitioners can learn from the KMcommunity. KMsystems are perhaps best viewedin a holistic systemthinking manner (Checkland & Howel, 1998) rather than the morerestrictive technologyviewof a system. As such, a KMsystem must support all activities that would encourage a knowledgesharing culture within a learning 3 eGain have recently acquired Inference pioneering CBRcompany Corp. a organisation. CBRonly provides methodological support to someof those activities. 6 References Aha, D., Becerra-Fernandez,I., Maurer, F. &MunozAvila, H. (1999). Exploring Synergies of Knowledge Managementand Case-Based Reasoning. AAAITechnical Report WS-99-10. ISBN1-57735-094-4 Aamodt,A. &Plaza, E. (1994). Case-BasedReasoning: FoundationalIssues, MethodologicalVariations, and SystemApproaches.AI Communications,7(i), pp. 39-59. Boisot, M. (1998). 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