Knowledge Elicitation Plug-In for Protégé: Card Sorting and Laddering

  • Yimin Wang
  • York Sure
  • Robert Stevens
  • Alan Rector
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4185)


Ontologies have been widely accepted as the primary method of representing knowledge in the Semantic Web. Knowledge Elicitation (KE) is usually one of the first steps in building ontologies. A number of ontology editors such as Protégé have been developed to assist users in building ontologies efficiently. However, traditional KE techniques, such as card sorting and laddering, are not yet supported, but performed manually and outside of such tools. In this paper we present a methodology and a corresponding plug-in for Protégé that allows graphical ellicitation knowledge from documents using card sorting and laddering approaches. Our aim is to seamlessly integrate the KE techniques into the ontology building process to make ontology building more efficient and less error-prone. As a side-effect the persistent storage of card sorting and laddering results allows for later traceability of ontology development. KE largely depends on user interaction with the plug-in, therefore we employed user-centred design principles to capture requirements. After implementation, the plug-in was evaluated thoroughly against the requirements. The evaluation shows that this KE plug-in meets many of the user’s expectations and indeed saves them considerable time when building ontologies.


Ontology Engineering Ontology Learning Knowledge Elicitation Ontology Editor IEEE Intelligent System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Upchurch, L., Rugg, G., Kitchenham, B.: Using card sorts to elicit web page quality attributes. IEEE Software 18, 84–89 (2001)CrossRefGoogle Scholar
  2. 2.
    Cooke, N.J.: Varieties of knowledge elicitation techniques. Int. J. Hum.-Comput. Stud. 41, 801–849 (1994)MATHCrossRefGoogle Scholar
  3. 3.
    Hinkle, D.: The change of personal constructs from the viewpoint of a theory of construct implications. PhD thesis, Ohio State University, Cited in: Bannister, D. and Fransella, F, Inquiring Man. Penguin, Harmondsworth (1965)Google Scholar
  4. 4.
    Noy, N.F., Sintek, M., Decker, S., Crubézy, M., Fergerson, R.W., Musen, M.A.: Creating semantic web contents with protégé-2000. IEEE Intelligent Systems 16, 60–71 (2001)Google Scholar
  5. 5.
    Knublauch, H., Fergerson, R.W., Noy, N.F., Musen, M.A.: The protégé owl plugin: An open development environment for semantic web applications. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298, pp. 229–243. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Boose, J.H.: Knowledge acquisition techniques and tools: Current research strategies and approaches. In: Proceedings of Fifth Generation Computer Systems, pp. 1221–1235 (1988)Google Scholar
  7. 7.
    Hoffman, R.R.: The problem of extracting the knowledge of experts from the perspective of experimental psychology. AI Magazine 8, 53–67 (1987)Google Scholar
  8. 8.
    Shadbolt, N., Hara, K.O., Crow, L.: The experimental evaluation of knowledge acquisition techniques and methods: history, problems and new directions. International Journal of Human-Computer Studies 51, 729–755 (1999)CrossRefGoogle Scholar
  9. 9.
    Rugg, G., Eva, M., Mahmood, A., Rehman, N., Andrews, S., Davies, S.: Eliciting information about organizational culture via laddering. Journal of Information System 12, 215–230 (2002)CrossRefGoogle Scholar
  10. 10.
    Milton, N.: PCPACK Toolkit (2003),
  11. 11.
    Schreiber, G., Wielinga, B.J., Akkermans, H., de Velde, W.V., Anjewierden, A.: CML: The CommonKADS conceptual modelling language. In: Steels, L., Van de Velde, W., Schreiber, G. (eds.) EKAW 1994. LNCS, vol. 867, pp. 1–25. Springer, Heidelberg (1994)Google Scholar
  12. 12.
    López, M.F., Gómez-Pérez, A., Sierra, J.P., Sierra, A.P.: Building a chemical ontology using methontology and the ontology design environment. IEEE Intelligent Systems 14, 37–46 (1999)Google Scholar
  13. 13.
    Sure, Y., Staab, S., Studer, R.: On-to-knowledge methodology. In: Staab, S., Sure, Y., Studer, R. (eds.) Handbook on Ontologies. Series on Handbooks in Information Systems, pp. 117–132. Springer, Heidelberg (2003)Google Scholar
  14. 14.
    Tempich, C., Pinto, H.S., Sure, Y., Staab, S.: An argumentation ontology for distributed, loosely-controlled and evolving engineering processes of ontologies (diligent). In: Gómez-Pérez, A., Euzenat, J. (eds.) ESWC 2005. LNCS, vol. 3532, pp. 241–256. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Shneiderman, B.: Designing the User Interface: Strategies for Effective Human-Computer Interaction. Addison-Wesley Longman Publishing Co., Inc., Boston (1997)Google Scholar
  16. 16.
    Cimiano, P., Völker, J.: Text2onto – a framework for ontology learning and data-driven change discovery. In: Montoyo, A., Muńoz, R., Métais, E. (eds.) NLDB 2005. LNCS, vol. 3513, pp. 227–238. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  17. 17.
    Lassila, O., Swick, R.: Resource Description Framework (RDF) Model and Syntax Specification. In: W3C Recommendation,World Wide Web Consortium, Boston, December 6 (2000),

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yimin Wang
    • 1
  • York Sure
    • 1
  • Robert Stevens
    • 2
  • Alan Rector
    • 2
  1. 1.Institute AIFBUniversity of KarlsruheGermany
  2. 2.School of Computer ScienceUniversity of ManchesterUK

Personalised recommendations