A Web-based CBR knowledge management system for PC
Contents
1. A user interface for queries and case knowledge acquisition A KM Module this module is mainly for acquiring case related knowledge so as to build up and maintain databases such as the case library the ontology li brary the similarity matrix library and the global vocabulary library A CBR engine When users input case attributes this processes the computing algorithm and prompts with similar cases for reference A case knowledge sharing converter the major func tion of this module is to offer standards for the translation and the mapping of domain knowledge elements WWW Server awug Yao 5 fes Se vem eE be ws Ery Browser Interface Client zx Fig 1 The structure of the Web based CBR model 534 Fig 2 The proposed architec ture of the Web based CBRKM system Client Browser Interface Structural knowledge tation tool Know Knowledge acquisition ser interface agent yuni ta was E EL Problem querying User interface agent Knowledge Elicitation Knowledge Sharing Converter Knowle edge Retrieva The CBR KM System Server Knowledge Organization vledge c and codin assification tool iing aes ritic based Adaptation Similarity assessment Acase library case knowledge stored in the case library includes case attribute indices a declarative vocabulary and proble2. 3 Damodaran L Olphert W 2000 Barriers and facilitators to the use of knowledge management systems Behav Info Technol 19 6 405 415 4 Kendall KE 1997 The significance of information systems research on emerging technologies seven information tech nologies that promise to improve managerial effectiveness Dec Sci 28 775 792 540 5 6 7 10 11 12 13 14 Schank RC 1982 Dynamic memory a theory of learning in computers and people Cambridge University Press Cambridge Riesbeck C Schank R 1989 Inside case based reasoning Lawrence Erlbaum Hillsdale Kolonder J Simpson RL 1989 The MEDIATOR analysis of an early case based problem solver Cog Sci 13 507 549 Hammond KJ 1989 Case based planning viewing planning as a memory task Academic Press Boston Yang H Lu WF Li AC 1992 A framework for using case based reasoning in automated process planning Conc Engin 59 101 114 Kwong CK Smith GF 1998 A computational system for process design of injection moulding combining a blackboard based expert system and a case based reasoning approach Int J Adv Manuf Technol 14 350 357 Hua K Faltings B 1993 Exploring case based Design CADRE Art Intellig Eng Des Anal Manufact 7 135 144 Sun SH Chen JL 1996 A fixture design system using case based reasoning Engin Appl Art Intellig 9 5 533 540 Humm B Schulz C Radtke M Warnecke G 1991 A system for case based process planning Comp I
3. The effectiveness of the Web based CBRKM system on KM activities was discussed Primary activities in the KM cycle are described below 1 Knowledge Capture Knowledge capture is the process by which knowledge is obtained and stored 23 The KM system developed in the study adopted CTA technique to build KA tools by which engineers in maintenance service centers can directly extract PC troubleshooting knowledge In particular significant savings in the energy and time necessary for knowledge retrieval can be realised with the help of software The troubleshooting knowledge extracted by maintenance engineers can then be systematically entered into the case library making it possible for maintenance knowledge scattered among various maintenance centres to be captured and transmitted Knowledge Development Once knowledge has been captured it must be organized and analyzed for strategic or tactical decision making Such applica tions are a means of gathering meaningful knowl edge from existing data stored in databases data warehouses and digital libraries 23 Using the CBR reasoning algorithm the system generates a matrix of similarities between cases from which a hierar chical clustering is processed to classify and structuralize the case knowledge Moreover the application of ontology techniques helps classify and encode the fault cases adding the practical value of gathering PC troubleshooting knowledge dispersed and tacit in
4. The evaluation indices proposed by Hsu et al 20 are used for the evaluation task to ensure the rationality and con sistency of clustering results These indices deter mine reasonable numbers of clusters and suitable clustering threshold values that serve as judgment criteria for automatic classification of new cases 2 535 3 2 3 Step 3 Building up the domain concept hierarchy A domain concept hierarchy is needed to represent the ontology structure In this study the domain concept hierarchy is defined using the object oriented approach A domain ontology defines roles and their relationships A role represents a real world concept while relation ships between roles are defined through two relations IS_A and HAS_A The IS_A relation shows subclass and inheritance For example X IS_A Y indicates X is a sub class of Y and inherits the attributes of Y and Y isa super class of X HAS_A relation represents a part whole relation For example X HAS_A Y indicates Y is an element of X A defined domain concept hierarchy forms a classification structure with varying degrees of abstractness and concreteness In such a structure con cepts on the abstract level usually provide less detailed information than those on the concrete level Moreover many concrete concepts may share abstract concepts These principles and specifications allow such a domain concept hierarchy to be applied to practical domains 3 2 4 Step 4
5. ETRIEVE process deals with the case similarity measure which compares query cases and old cases to find the cases most likely to be useful while the case indexing procedure provides an efficient way to search for candi dates Users need not understand the relationship between the description and solution parts since an automatic reasoning algorithm feeds back proposed solutions The case retrieval algorithm Eq 1 described in the study was mainly derived from an algorithm proposed by The KM Activities Support The CBR KM System Components Knowledge Capture Knowledge Acquisition Module Knowledge Elicitation Tool E Case Library E Case Based Reasoning Engine E Knowledge Acquisition Module Case Knowledge Classification and Coding Tool Knowledge Development E Web Based CBR Model HOntology Based User Interface E Knowledge Sharing Converter Web Based CBR Model E Ontology Based User Interface Fig 3 The relationship between the system components and KM activity in the CBRKM system Knowledge Sharing Knowledge Utilization Janet Kolodner 16 that determines similarities between cases and identifies those with higher similarity values Y W x Sim 1 48 1 UM i l n the number of attribute indexes W the weighting value of each attribute index f f newly entered case fe case in the case library Sim f fP the similarity between the entered case and the case in the case libra
6. Formalizing the case knowledge ontology A semantic hierarchical structure for groups of cases is constructed based on the domain concept hierarchy in this step The structure starts with the lowest concept level and gradually goes to higher levels until the semantic structures of all groups are organized The combination of group semantic structures completes the ontology of the application domain 3 2 5 Step 5 Evaluation To evaluate the case knowledge ontology the present software environment and documents will be used to assess how to build up the ontology through program ming languages 4 Case study PC troubleshooting The knowledge pattern in an industry can be know how maintenance facts product requirements design ratio nale experience or professional knowledge Among them know how is an important element in that it contains problem solving expertise in functional disci plines experience of human resources process experi ence design issues and lessons learned However such knowledge must be accumulated through systematic acquisition and storage It is therefore a fundamental job for industries to systematically integrate dispersed know how when building up KM systems In this study the authors used the PC troubleshooting maintenance centres of a computer company as an example of applying a systematic method for extracting trouble 536 shooting know how and maintenance facts to build up a PC troubleshooting
7. Int J Adv Manuf Technol 2004 23 532 540 DOI 10 1007 s00170 003 1676 0 ORIGINAL ARTICLE o O ARTICLE S L Wang S H Hsu A Web based CBR knowledge management system for PC troubleshooting Received 11 November 2002 Accepted 13 February 2003 Published online 12 February 2004 Springer Verlag London Limited 2004 Abstract Using case based reasoning CBR the authors integrate the techniques of cognitive task analysis CTA hierarchical clustering and ontology and pro pose a Web based CBR knowledge management KM system for investigating the construction of a KM sys tem with multiple information techniques to support KM activity in industry The maintenance service cen tres of a computer company are used as an example to illustrate extracting the maintenance knowledge neces sary to construct a PC troubleshooting KM system The effectiveness of applying a Web based CBR KM system to support KM activities in the KM life cycle is sub jected to practical verification Keywords Case Based Reasoning CBR KM System Cognitive task analysis CTA Ontology Hierarchical Clustering 1 Introduction The concept of knowledge management KM was pointed out in the early 1990s Only recently however has it received attention in the practical industrial do main primarily because KM and innovative knowledge are becoming too important for industries to ignore in facing global competition Organizations according t
8. KM system Knowledge elements in this study are described according to case patterns In general they consist of declarative case knowledge and structural case knowl edge Declarative case knowledge consists of two parts a description part and a solution part The description part describes case attributes via certain indexes The solution part is the major case knowledge component contains the know how Structural case knowledge contains similarities among case attributes 4 1 Troubleshooting knowledge acquisition The methods for case knowledge acquisition were as follows 1 Structured interviews and concept elicitation methods 21 were applied to acquire declarative case knowledge including possible attribute fea tures attribute indices and descriptive trouble shooting vocabulary Structured interviews and cognitive task analysis methods 21 were used to acquire the subjects problem solving knowledge The authors applied the GOMS Goal operators methods and selection rules developed by Card et al 22 in which a series of open ended questions are used to lead subjects to verbally report on troubleshooting processes The authors applied rating tasks to evaluate case attribute similarities Three case attribute similarity matrices for the fault attribute indices were ob tained which enabled calculation of further case similarity matrices 2 3 4 2 Constructing the hierarchical case knowledge classificatio
9. bleshooting and diagnosis The effectiveness of using Web based CBR to enhance KM activities was also assessed 2 System architecture The authors applied CBR to develop a Web based CBR model and finally a KM system and an approach that has the following advantages Experience knowledge scattered among various sites in different areas can be integrated in a unified format CBR allows the continuous updating and adaptation of corporate memory This refines and enriches knowledge library content and builds up the KM system The more problems the CBR system solves the wider the scope of problems it can cover Recycling of experience knowledge in the same problem domains will reduce the incidence of trial and error Because CBR resembles human reasoning the prob lem solving ability of an organization s professional personnel is upgraded with CBR support Using CBR as a real time Internet consultant can enhance knowledge communication and sharing among employees as well as the organizational learning environment 2 1 The structure of the Web based CBR model Schank 5 and Riesbeck and Schank 6 advocated CBR and referred to it as an alternative to traditional rule based and model based reasoning In recent years CBR techniques have been applied to a wider range of prob lem domains including catering recipe making dispute mediation criminal sentencing and process planning 7 8 9 Various computer a
10. different maintenance centres This helps maintenance engineers enhance their troubleshoot ing efficiency Furthermore the shortcomings of having such knowledge scattered and lacking in structure for reference value are also reduced Knowledge Sharing Once knowledge has been analyzed distribution and sharing is the next nec essary step in the process of KM With the devel opment of KA tools tactic knowledge scattered among maintenance service centers can be orga nized and encoded for storage in the case library and ontology library Maintenance engineers indifferent centres can communicate and share their 2 3 539 troubleshooting experience through computer assisted telecommunications and the Web based CBR model This enhances the learning environ ment in the organization Knowledge Utilization The last step in KM is to effectively encourage employees to use knowledge It requires vast financial resources and time commit ments for organizations to build knowledge based systems Accordingly information systems should be developed for end user convenience and make it easy for users to manipulate knowledge The authors built a hierarchical case knowledge classification struc ture which was developed into a case knowledge KM system through a graphic ontology based user interface With the ontology based user interface maintenance personnel can easily retrieve and use PC troubleshooting knowledge thus enhancing the eff
11. down enatically goz Power Supply connection p A cluttered E22 Data cable connection prokl Hard iD Hard drivey DR eon P hard disk B 0 78 B apie makes high pitched noise 7 Anti static wiper is defectiy Floppy and Drive spins Power Supply PA E0 Heads have crashed 00028 Hard Disk Hard drive podo poem 0 65 errors y is egw performance S 31 ea Floppy and Drive spins Data cable gOS Sector eave pobles 19929 Hard Disk Hard drive upanddown Connection 0 65 E2 Controller problems J errors erratically problems ge BUFFERS setting is wrong s Drive won t start a Drive motor Egape fi up fst 0026 Hard Disk Hard drive spinning or it me Cher Sized B A dunesed hand disk Floppy and and T Setor ggFlerpy Disk Hard Disk Hard drive Slow performance interleave 8 gpro message Track 0 Bad emors o problems Bad disk wd VIED Tan ene Manes dante a fr EEO ia Case Knowledge Sharing Converter This module provides translation and contrast standards for declarative case knowledge The standardised inter preter makes enables various maintenance service centers to communicate and share PC troubleshooting knowledge Case library Troubleshooting case knowledge is stored in the case library where case attributes serve to index all cases Methods and knowledge needed for PC troubleshooting are recorded for every case Fig 4 5 Discussions
12. ectiveness of the retrieval sharing and usage of troubleshooting knowledge 4 6 Conclusions The authors used a Web Based CBR KM system structure to build a prototype of PC troubleshooting KM system It has been found that organizations need to integrate different methods and techniques in devel oping KM systems so as to uplift the effectiveness of KM activities With the rapid development of industrial techniques it is necessary for industrial organizations to realize how to hand over their experience and maintenance knowl edge through a KM system so as to upgrade their innovation and development abilities In the study various information techniques were integrated to build an information KM system for an enterprise The KM system construction and structure proposed in the study may serve as a guide for industrial organizations to de velop KM systems However it is not easy to assess the effectiveness of introducing a KM system to an organization in a short period of time It is necessary to conduct long term evaluations and improvements to make the KM system meet the organization s needs References 1 Grundstein M Barth s JA 1996 An industrial view of the process of capitalizing knowledge In Schreinmakers JF ed Knowledge management organization competence and methodology Ergon W rzburg 2 Ong SK An N Nee AYC 2001 A Web based fault diagnostic and learning system Int J Adv Manuf Technol 18 502 511
13. ions and error messages emitted by the computer The lowest levels are the most concrete and detailed information in the hierarchy Formalise ontology At this stage coding of the case knowledge is translated into concrete form 3 4 5 537 Table 1 A comparison of expert classifications and Expert Hierarchical Clustering in different threshold values various clustering in different Classification XDE threshold values type Classification g_ 0 0 66 6 0 47 0 0 32 0 0 17 Case Clustering Number of Groups Appropriate number 0 429 0 571 0 857 0 651 0 186 of group r a 28 51 0 549 38 51 0 745 46 51 0 902 34 51 0 667 13 51 0 254 According to the domain concept hierarchy the opinions of maintenance experts and the ontology lowest levels of the seven groups were first exam of PC trouble shooting was built up ined for semantic structures which were then combined to form the hierarchical case knowledge classification structure From the hierarchical case 4 3 System functions and operation knowledge classification structure the users can connect to the troubleshooting knowledge in the A PC troubleshooting CBR KM system was developed case library Finally the authors integrated the on the basis of the Web based CBRKM system archi The level of homogeneity h 538 tecture System functions and operation are described maintain the case library KA tools provided in below User Interface An on
14. m solution knowledge KM activities during the KM cycle include knowl edge capture knowledge development knowledge sharing and knowledge utilization and each of the system components plays a role in those activities The KA module and the case library handle knowledge acquisition support The CBR engine and knowledge classification and coding tools handle organization and development enabling case knowledge to be translated into suitable formats for sharing and retrieval The case knowledge sharing converter changes terminology in different units into standard vocabulary so that knowledge from different sources can be communicated and shared More importantly adoption of the Web based CBR model helps dissemination of knowledge Above all use of an ontology based interface enhances the reuse and sharing of knowledge The relationship between the functional module and the KM activity in KM life cycle in the CBRKM system is illustrated in Fig 3 3 Implementation approach In the study the Java programming language and a dynamic server Web page were used to construct the Web based CBRKM system because they are platform independent Internet supported and suitable for devel oping KM systems for the Internet The CBR reasoning mechanism and ontology techniques were employed in constructing the case knowledge KM system Related methods and processes are described in the following section 3 1 The Case retrieval algorithm The R
15. n structure A hierarchical case knowledge classification structure for PC troubleshooting was built up based on the case knowledge organization approach described in Sect 3 2 The algorithm for doing so consists of the following steps 1 Extract case attribute index and declarative vocab ulary Important PC troubleshooting case attributes such as fault condition fault position and fault symptom were first extracted and a declarative vocabulary for these attribute indices identified For example fault condition vocabulary might include no display system failed to start and failed to connect to the Internet etc fault position vocabu lary might include CPU power supply hard drive etc and fault symptom vocabulary might include CMOS RAM error games and programs run too fast Windows protection error etc Calculate case similarities After the declarative case attribute vocabulary was extracted attribute 2 Tree Diagram for 51 Variables The average linkage agglomerative analysis 00 83 00 66 00 47 00 32 00 17 0 OO OT NII OOOO COOP ATO Sg SEES RAE SEED SPOOR IOTA DOT OT IIIA TANNE OR OOOO TOONT TTT Fig 4 Hierarchical clustering outcomes weighting values for troubleshooting were deter mined by maintenance experts to be fault condition 33 fault location 15 and fault symptom 52 The experts were also asked to rate the
16. ndust 17 169 180 Lee KS Luo C 2002 Application of case based reasoning in die casting die design Int J Adv Manuf Technol 20 4 284 295 21 22 23 Sengupta A Wilson DC Leake DB 1999 On constructing the right sort of CBR implementation In Proceedings of the IJ CAI 99 workshop on automating the construction of case based reasoners Stockholm Sweden August 1999 Kolodner J 1993 Case based reasoning Morgan Kaufmann San Mateo Khan L McLeod C 2000 Audio structuring and personalized retrieval using ontologies IEEE Proceedings of advances in digital libraries Washington DC 22 24 May 2000 Uschold M Gr ninger M 1996 Ontologies principles methods and applications Know Engin Rev 11 93 136 Dubes RC Jain AK 1988 Algorithms that cluster data Pre ntice Hall Englewood Cliffs Hsu SH Hsia TC Wu MC 1997 A flexible classification method for evaluating the utility of automated workpiece classification system Int J Adv Manuf Technol 13 637 648 Cordingley E 1989 Knowledge elicitation techniques for knowledge based systems In Diaper D ed Knowledge elici tation Ellis Harwood Chichester Card SK Moran TP Newell A 1983 The psychology of hu man computer interaction Lawrence Erlbaum Hillsdale Lee SM Hong S 2002 An enterprise wide knowledge man agement system infrastructure Ind Manage Data Syst 17 25
17. o Grundstein and Barth s 1 are made up not only of their products and service units but also of their knowledge assets It is therefore necessary for industrial units to build up KM systems appropriate to their scales and requirements Such knowledge systems can provide benefits in the following ways S L Wang S H Hsu lt Institute of Industrial Engineering National Chiao Tung University Hsinchu Taiwan ROC E mail shhsu cc nctu edu tw Tel 886 3 5726731 Fax 886 3 5722392 preventing the loss of know how when professionals leave the organization taking advantage of knowledge and techniques pre viously gained from experience so as not to re make mistakes developing organizational knowledge maps that can serve as guidelines in making manufacturing strate gies helping with information cycling and communication among various units enhancing employee learning environments integrating know how from various sources in orga nizations Nowadays many manufacturers are facing serious structural problems brought about by the rapid devel opment of overseas activities such as factories 2 branch companies and manufacturing facilities set up in various areas to meet business expansion requirements Facilities located in different regions greatly split core knowledge and make it more difficult to carry out KM activities It is therefore worthwhile to conduct an in depth investiga
18. ry 3 2 The case knowledge organization approach Ontology is a collection of key concepts and their inter relationships that collectively provide an abstract view of an application domain 17 18 With the support of ontology users can communicate with one another and with the system with a shared and common understanding of domain knowledge In the study the algorithm pro posed by Uschold and Griininger 18 was used as a foundation for developing the case knowledge organiza tion approach Practically speaking the organisation of case knowledge can be categorized into five steps data preprocessing structuralizing case knowledge building up the domain concept hierarchy formalising ontology and evaluation Details on how case knowledge is orga nized are given below 3 2 1 Step Data preprocessing 1 Capture indices and descriptive case attribute vocabularies Important case attribute indices as well as their vocabularies are extracted during this stage Rate case attributes to determine attribute similar ity values The degrees of similarity between cases are then computed according the CBR algorithm 2 3 2 2 Step2 Structuralizing case knowledge 1 Categorize cases put in preliminary groups by hierarchical clustering 19 Using hierarchical clustering allows homogeneous cases to be grouped according to their clustering threshold values and also allows for processing noisy data Evaluate case clustering results
19. similarities between case attributes so as to build up a matrix of case attribute similarities The matrix of case simi larities was then computed using the CBR algo rithm Structuralize case knowledge An average linkage agglomerative analysis of the hierarchical clustering was first conducted and the output can be seen in Fig 4 The clustering rationality and consistency indexes proposed by Hsu et al 20 were used to assess the consistency and rationality of clustering results derived from computed clustering threshold values and the sorting done by the experts A suit able clustering threshold value was determined by evaluating a data set of 51 training items The re sults are shown in Table 1 When the clustering threshold value 0 was 0 47 the rationality index of the clustering output was 0 857 and the consistency index 0 902 These indices were the highest among the clustering threshold values Through hierarchi cal clustering analysis cases were divided into seven groups and the clustering threshold value criterion for automatic classification of new cases was set at 0 47 Build up the domain concept hierarchy The object oriented approach was used to build up the PC troubleshooting domain conceptual hierarchy In the classification structure fault condition was the first level The second level is the fault position meaning the positions that are disabled The third level fault symptom represents specific problem indicat
20. ssisted systems have been developed for industrial tasks such as in injection moulding and design 10 architecture design 11 fix ture design 12 process planning 13 and die casting die design system 14 According to Sengupta Wilson and Leake 15 we can identify three CBR implementation models task based enterprise based and Web based The task based model is a traditional CBR system designed for a specific task and doesn t include knowledge sharing functions The enterprise model is a system constructed for an enterprise to manage proprietary knowledge such as 533 project experience problem solving methods etc As its name suggests the Web based model breaks geograph ical barriers through the World Wide Web WWW thus making it possible for scattered enterprise units to share knowledge On account of its characteristics a Web based CBR model was employed in the present study to help build up the CBR distribution system Intelligent Web based case assistants were designed using a thin client struc ture Communication between client and server as well as the user interface was implemented at the client end All the business logic and the logic integrating the two ends was confined to the server end The structure of the Web based CBR model is shown in Fig 1 The pro posed architecture is shown in Fig 2 2 2 The CBRKM system structure The KM system proposed in this study consists of the following components
21. tion into how divergent industrial knowledge can be systematically integrated so as to obtain effective KM The rapid development of infor mation handling techniques over the past decade has made knowledge based systems including expert sys tems corporate memory systems information systems and other advanced information resources indispensable to organizations seeking effective KM 3 Though there are many researchers dedicating them selves to the development of KM techniques there is currently no single information system that supports all the activities in the KM cycle Typically many individual information systems supporting various KM activities are offered Many such KM systems put considerable emphasis on the knowledge storage and memory aspects However Kendall 4 pointed out that it is necessary to integrate related information techniques in the develop ment of KM systems to guarantee that all activities in the KM cycle are sufficiently supported To ensure the effective integration of information techniques the authors combined CTA ontology tech niques and a Web based case based reasoning CBR model to develop a KM system Multiple techniques were incorporated to support industrial KM activities including knowledge capture knowledge development knowledge sharing and knowledge utilization More over a computer company was used as a practical case to investigate extracting the maintenance know how required for PC trou
22. tology based user interface en coded in programming language was developed With such a graphical interface users can search for and re trieve needed case knowledge The user interface for case knowledge retrieval is shown in Fig 5 KA Module The purpose of this module was to re trieve related domain knowledge so as to build and Fig 5 The user interface for case knowledge retrieval Fig 6 The output display of case reasoning the system include the case attribute rating tool the declarative case knowledge retrieval tool and the automatic case knowledge classification and coding tool CBR Engine When users select fault attributes via the user interface the system automatically processes the reasoning algorithm and lists similar cases Fig 6 shows the output display after case reasoning a zo gt gt OO G Que mmer Fue 3V 30 A Ehe lommon Floppy and Hard Disk Errors g giad Dave i N ENEAS AT al i Drive motor or electronics Er agg ve sas op ad down ealy La Power Supply connection p Data cable connection i see males Nek et ee ene Anti static wiper is defectiv Case library query Chaser Sia EES A cluteved hand disk bonne Eh gqPn screen message Track 0 Bad Bad disk T EA at el gt i amp E AS up fast Jaro je HER gt O A Que TRE a 3D 30 A oy z eS po ae Outcome of CBR _ 8 pinana o ls motor Or electronics 8 Pie spins up and
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