A Methodology for Developing High Quality Ontologies for Knowledge Management

Part of the Integrated Series in Information Systems book series (ISIS, volume 35)


Ontologies have been identified as important components of Knowledge Management Systems (KMS), and the quality of such systems is therefore likely to be heavily dependent on the quality of the embedded ontology. This chapter describes an approach to the development, representation, and evaluation of formal ontologies with the explicit aim being to develop a set of techniques that will improve the coverage of the ontology, and thus its overall quality. This will ensure that when the ontology is integrated into the KMS it will not jeopardize the quality of the system as a whole. The proposed approach will be illustrated by applying it to the development and evaluation of an ontology that can be used as a component of a KMS for the information technology (IT) infrastructure at a university campus.


Ontology Knowledge management systems Knowledge acquisition 



Portions of this chapter have appeared in “An Approach for Ontology Development and Assessment using a Quality Framework,” Knowledge Management Research & Practice (2009) 7, 260–276.


  1. Alavi, M., & Leidner, D. E. (2001). Review: Knowledge management and knowledge management systems: Conceptual foundations and research issues [Article]. MIS Quarterly, 25(1), 107.CrossRefGoogle Scholar
  2. Bhatia, S., & Yao, Q. (1993). A new approach to knowledge acquisition by repertory grids. Paper presented at the second international conference on information and knowledge management, Washington, DC.Google Scholar
  3. Burton-Jones, A., Storey, V. C., Sugumaran, V., & Ahluwalia, P. (2005). A semiotic metrics suite for assessing the quality of ontologies. Data & Knowledge Engineering, 55, 84–102.CrossRefGoogle Scholar
  4. Chen, C.-H., Khoo, L. P., & Yan, W. (2002). A strategy for acquiring customer requirement patterns using laddering technique and ART2 neural network. Advanced Engineering Informatics, 16(3), 229–240.CrossRefGoogle Scholar
  5. Cooke, N. J. (1999). Knowledge elicitation. In F. T. Durso (Ed.), Handbook of applied cognition (pp. 479–509). Chichester: Wiley.Google Scholar
  6. Curran, M., Rugg, G., & Corr, S. (2005). Attitudes to expert systems: A card sort study. The Foot, 15, 190–197.CrossRefGoogle Scholar
  7. Fensel, D., van Harmelen, F., Universiteit, V., Horrocks, I., McGuinness, D., & Patel-Schneider, P. (2001). OIL: An ontology infrastructure for the semantic web. IEEE Intelligent Systems, 16(2), 38–45.CrossRefGoogle Scholar
  8. Fox, M. S., Barbuceanu, M., Gruninger, M., & Lin, J. (1998). An organization ontology for enterprise modeling. In M. Prietula, K. Carley, & L. Gasser (Eds.), Simulating organizations: Computational models of institutions and groups (pp. 131–152). Menlo Park, CA: AAAI/MIT Press.Google Scholar
  9. Fox, M. S., & Gruninger, M. (1998). Enterprise modelling. AI Magazine, 19(3), 109–121.Google Scholar
  10. Gangemi, A. (2005). Ontology design patterns for semantic web content. Paper presented at the 4th international semantic web conference (ISWC 2005), Galway, Ireland.Google Scholar
  11. Gruninger, M., & Fox, M. S. (1994). The design and evaluation of ontologies for enterprise engineering. Paper presented at the workshop on implemented ontologies, European conference on artificial intelligence (ECAI), Amsterdam, NL.Google Scholar
  12. Gruninger, M., & Fox, M. S. (1995). Methodology for the design and evaluation of ontologies. Paper presented at the IJCAI ‘95, workshop on basic ontological issues in knowledge sharing, Montreal, QC, Canada.Google Scholar
  13. Guarino, N. (1998). Formal ontology and information systems. Paper presented at the first internation conference on formal ontologies in information systems, Trento, Italy.Google Scholar
  14. Guarino, N., Carrara, M., & Giaretta, P. (1994). An ontology of meta-level categories. Paper presented at the fourth international conference on principles of knowledge representation and reasoning (KR ‘94), Bonn, Germany.Google Scholar
  15. Guenzi, P., & Troilo, G. (2006). Developing marketing capabilities for customer value creation through marketing–sales integration. Industrial Marketing Management, 35, 974–988.CrossRefGoogle Scholar
  16. Harper, M. E., Jentsch, F. G., Berry, D., Lau, H. C., Bowers, C., & Salas, E. (2003). TPL–KATS-card sort: A tool for assessing structural knowledge. Behavior Research Methods, Instruments, & Computers, 35(4), 577–584.CrossRefGoogle Scholar
  17. Hassenzahl, M., & Trautmann, T. (2001). Analysis of web sites with the repertory grid technique. Paper presented at the human factors in computing systems, Seattle, WA.Google Scholar
  18. Hickey, A. M., & Davis, A. M. (2003). Elicitation technique selection: How do experts do it? Paper presented at the requirements engineering conference, Monterey Bay, CA.Google Scholar
  19. Hölscher, C., & Strube, G. (2000). Web search behavior of internet experts and newbies. Computer Networks, 33(1–6), 337–346.CrossRefGoogle Scholar
  20. Jarke, M., Jeusfeld, M., Quix, C., & Vassiliadis, P. (1999). Architecture and quality in data warehouses: An extended repository approach. Information Systems, 24(3), 229–253.CrossRefGoogle Scholar
  21. Joshi, H., Seker, R., Bayrak, C. S. R., & Connelly, J. (2007). Ontology for disaster mitigation and planning. Paper presented at the summer computer simulation conference, San Diego, CA.Google Scholar
  22. Kaneiwa, K., & Mizoguchi, R. (2004, June 2–5). Ontological knowledge base reasoning with sort-hierarchy and rigidity. Paper presented at the ninth international conference on the principles of knowledge representation and reasoning (KR2004), Whistler, BC, Canada.Google Scholar
  23. Kemp, E. A. (1996). The role of the individual project in teaching knowledge acquisition. Paper presented at the international conference on software engineering: Education and practice (SE:EP ‘96), Dunedin, New Zealand.Google Scholar
  24. Kim, H. M., Fox, M. S., & Sengupta, A. (2007). How to build enterprise data models to achieve compliance to standards or regulatory requirements (and share data). Journal of the Association for Information Systems, 8(2), 105–128.Google Scholar
  25. Kwan, M., & Balasubramanian, P. (2003). KnowledgeScope: Managing knowledge in context. Decision Support Systems, 35(4), 467.CrossRefGoogle Scholar
  26. Mansingh, G., Osei-Bryson, K.-M., & Reichgelt, H. (2009). Building ontology-based knowledge maps to assist knowledge process outsourcing decisions. Knowledge Management Research and Practice, 7, 37–51.CrossRefGoogle Scholar
  27. Nakhimovsky, Y., Schusteritsch, R., & Rodden, K. (2006). Scaling the card sort method to over 500 items: Restructuring the Google AdWords Help Center. Paper presented at the conference on human factors in computing systems CHI ‘06 Montréal, QC, Canada.Google Scholar
  28. Noy, N. (2004). Semantic integration: A survey of ontology based approaches. SIGMOD Record, 33(4), 65–69.CrossRefGoogle Scholar
  29. Noy, N., & Hafner, C. (1997). The state of the art in ontology design. AI Magazine, 18(3), 53–74.Google Scholar
  30. Osei-Bryson, K.-M., Millar, H., Joseph, A., & Mobolurin, A. (2002). Using formal MS/OR modeling to support disaster recovery planning. European Journal of Operational Research, 141(3), 679–688.CrossRefGoogle Scholar
  31. Peffers, K., & Gengler, C. E. (2003). How to identify new high-payoff information systems for the organization. Communications of the ACM, 46(1), 83–88.CrossRefGoogle Scholar
  32. Pike, S. D. (2003). The use of repertory grid analysis to elicit salient short break holiday attributes. Journal of Travel Research, 41(3), 326–330.CrossRefGoogle Scholar
  33. Pinto, H. S., & Martins, J. P. (2004). Ontologies: How can they be built? Knowledge and Information Systems, 6(4), 441–464.CrossRefGoogle Scholar
  34. Pirolli, P. (2006). Assisting people to become independent learners in the analysis of intelligence: Final technical report (CDRL A002). Arlington, CA: Palo Alto Research Center.Google Scholar
  35. Rao, L., & Osei-Bryson, K.-M. (2007). Towards defining dimensions of knowledge systems quality. Expert Systems with Applications, 33(2), 368–378.CrossRefGoogle Scholar
  36. Reichgelt, H., & Shadbolt, N. (1992). ProtoKEW: A knowledge-based systems for knowledge acquisition. In D. Sleeman & O. Bernsen (Eds.), Research advances in cognitive science: Artificial intelligence (Vol. 5, pp. 171–200). Mahwah, NJ: Lawrence Erlbaum.Google Scholar
  37. Ryan, G. W., & Bernard, H. R. (2000). Data management and analysis methods. In N. Denzin & Y. Lincoln (Eds.), Handbook of qualitative research (pp. 769–802). Thousand Oaks, CA: Sage.Google Scholar
  38. Shadbolt, N., & Burton, A. M. (1989). The empirical study of knowledge elicitation techniques. ACM SIGART Bulletin, 108, 15–18.CrossRefGoogle Scholar
  39. Sharma, S., & Osei-Bryson, K.-M. (2008, January 7–10). Organization-ontology based framework for implementing the business understanding phase of data mining projects. Paper presented at the 41st annual hawaii international conference on system sciences, Big Island, Hawaii.Google Scholar
  40. Sicilia, M.-A., Lytras, M., Rodriguez, E., & Garcia-Barriocanal, E. (2006). Integrating descriptions of knowledge management learning activities into large ontological structures: A case study. Data & Knowledge Engineering, 57(2), 111–121.CrossRefGoogle Scholar
  41. Staab, S., Schnurr, H.-P., Studer, R., & Sure, Y. (2001). Knowledge processes and ontologies. IEEE Intelligent Systems, 16(1), 26–34.CrossRefGoogle Scholar
  42. Sure, Y., Erdmann, M., Angele, J., Staab, S., Studer, R., & Wenke, D. (2002). OntoEdit: Collaborative ontology development for the semantic web. Paper presented at the first international semantic web conference (ISWC 2002), Sardinia, Italy.Google Scholar
  43. Upchurch, L., Rugg, G., & Kitchenham, B. (2001). Using card sorts to elicit web page quality attributes. IEEE Software, 18(4), 84–89.CrossRefGoogle Scholar
  44. Vassiliadis, P., Bouzeghoub, M., & Quix, C. (2000). Towards quality-oriented data warehouse usage and evolution. Informations Systems, 25(2), 89–115.CrossRefGoogle Scholar
  45. Wagner, W. P., & Zubey, M. L. (2005). Knowledge acquisition for marketing expert systems based upon marketing problem domain characteristics. Marketing Intelligence & Planning, 23(4), 403–416.CrossRefGoogle Scholar
  46. Walther, C. (1988). Many-sorted unification. Journal of the ACM, 35(1), 1–17.CrossRefGoogle Scholar
  47. Wand, Y., & Wang, R. Y. (1996). Anchoring data quality dimensions in ontological foundations. Communications of the ACM, 39(11), 86–95.CrossRefGoogle Scholar
  48. Wang, R. Y., Storey, V. C., & Firth, C. P. (1995). A framework for analysis of data quality research. IEEE Transactions on Knowledge and Data Engineering, 7(4), 623–640.CrossRefGoogle Scholar
  49. Wang, Y., Sure, Y., Stevens, R., & Rector, A. (2006). Knowledge elicitation plug-in for Protégé: Card sorting and laddering. Paper presented at the Asian semantic web conference (ASWC ‘06), Beijing, China.Google Scholar
  50. Wold, G. H. (2002). Disaster recover planning process. Disaster Recovery Journal, 5(1), 29–34.Google Scholar
  51. Zhang, H., Kishore, R., Sharman, R., & Ramesh, R. (2007). Agile integration modeling language (AIML): A conceptual modeling grammar for agile integrative business information systems. Decision Support Systems, 44(1), 266–284.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Lila Rao
    • 1
  • Han Reichgelt
    • 2
  • Kweku-Muata Osei-Bryson
    • 3
  1. 1.Mona School of Business and ManagementThe University of the West IndiesMona CampusJamaica
  2. 2.School of Computing and Software EngineeringSouthern Polytechnic State UniversityMariettaUSA
  3. 3.Department of Information SystemsVirginia Commonwealth UniversityRichmondUSA

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