Designing operators for constructing domain knowledge ontologies

Long Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1319)


Many researchers agree that the reuse of ontological components is an important research area in Knowledge Acquisition. However, it has been argued that there is still much to do in this field. For example, one of the topics that requires more research is that of developing methods to build ontologies. In this sense, it is important to define formal methods which provide us with operative frameworks to build ontologies. In this paper, for problems that satisfy a well-defined set of assumptions, we propose a mathematical approach that permits to build domain knowledge ontologies. We present such an operative framework based on both Sets Theory and mereological considerations. The approach comprises a set of ontological operators to extract domain knowledge. Finally, an example is put forward showing the application of these ontological operators.


Knowledge Acquisition Domain Ontology Knowledge Function Knowledge Elicitation Classical Mereology 
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. Alberts, L. K. (1993). YMIR: an ontology for engineering design, PhD Thesis, University of Twente.Google Scholar
  2. Benjamins, V.R. (1993). Problem Solving Methods for diagnosis, PhD Thesis, University of Amsterdam.Google Scholar
  3. Borst, P., and Akkermans, H. (1997). Engineering Ontologies, International Journal ofHuman-Computer Studies, 46: 365–406.CrossRefGoogle Scholar
  4. Chandrasekaran, B. (1987). Towards a functional architecture for intelligence based on generic information processing tasks, In Proceedings of the 10th IJCAI, 1183-1192, Milan, Italy.Google Scholar
  5. Cooke, N. J. (1994).Varieties of knowledge elicitation techniques, International Journal ofHuman-Computer Studies, Vol. 41:801–849.CrossRefGoogle Scholar
  6. Cordingley, E. S. (1989). Knowledge elicitation techniques for knowledge-based systems, In D. Diaper Ed. Knowledge Elicitation: Principles, Techniques, and Applications, 89–175, New York: John Wiley and Sons.Google Scholar
  7. Eschenbach, C., and Heydrich, W. (1995). Classical mereology and restricted domains, International Journal ofHuman-Computer Studies, 43: 723–740.CrossRefGoogle Scholar
  8. Gruber, T.R (1993). A Translation Approach to Portable Ontology Specifications, Knowledge Acquisition, 5 (2): 199–220CrossRefGoogle Scholar
  9. Gruber, T.R. (1994). Towards principles for the design of ontologies used for knowledge sharing, In N. Guarino and R. Poli (Eds.), Formal Ontology in Conceptual Analysis and Knowledge Representation, Boston, MA: Kluwer.Google Scholar
  10. Guarino, N. (1997). Understanding, building and using ontologies, International Journal ofHuman-Computer Studies, 46:293–310.CrossRefGoogle Scholar
  11. Guarino, N., and Giaretta, P. (1995). Ontologies and knowledge bases: towards a terminological clarification, In Mars, N. Ed., Towards Very Large Knowledge Bases: Knowledge Building and Knowledge Sharing 1995, 25–32, Amsterdam, IO Press.Google Scholar
  12. Leonard, H.S., and Goddman, N. (1940). The calculus of individuals and its uses, Journal of Symbolic Logic, 5: 45–55.Google Scholar
  13. Lesniewski, S. (1916). Foundations of a general theory of manifolds (in Czech), Prace Polskiego Kola Naukowe w Moskwie, Sekcya matematycznoprzyrodnicza, 2, Moscow.Google Scholar
  14. Martinez-Bójar, V.R., Benjamins, R., Martin, F., and Castillo, V. (1996). Deriving formal parameters for comparing knowledge elicitation techniques based on mathematical functions, In B. R. Gaines and M. Musen (Eds.), Proceedings of the 10th Banff Knowledge Acquisition for Knowledge-Based Systems Workshop, Vol. 2: 59.1–59.20, Banff, Canada.Google Scholar
  15. Martinez-Béjar, R, Cädenas, J. M., and Martin-Rubio, F.(1997). Fuzzy Logic in Landscape Assessment, In Proceedings of the European Symposium on Intelligent Techniques, 234–238, Bari, Italy.Google Scholar
  16. Martinez-Béjar, R., and Martin-Rubio, F. (1997). A mathematical functions-based approach for analysing elicited knowledge, To appear in Proceedings of the Ninth International Conference on Software Engineering and Knowledge Engineering, Madrid, Spain.Google Scholar
  17. Musen, M. A. (1989). Automated support for building and extending expert models, Machine learning, 4: 347–376.Google Scholar
  18. O'Hara, K., Motta, E., and Shadbolt, N. (1994). Grounding GDMs: A Structured Case Study, International Journal ofHuman-Computer Studies, Vol. 40: 315–347.CrossRefGoogle Scholar
  19. Puerta, A. R., Egar, J., Tu, S., and Musen, M. (1992). A multiple-method shell for the automatic generation of Knowledge acquisition tools, Knowledge Acquisition, 4:171–196.CrossRefGoogle Scholar
  20. Schreiber, A. T. (1993). Operationalizing models of expertise, In A. T. Schreiber, B. J. Wielinga, and J. A. Breuker (Eds.), KADS. A Principled Approach to Knowledge-Based System Development, 119–149, London: Academic Press.Google Scholar
  21. Schreiber, A. T., Wielinga, B:J, and Jansweijer, W.H.J (1995). The KACTUS View on the ‘O’ Word, In D. Skuce, N. Guarino and L. Bouchard (Eds) IJCAI Workshop on Basic Ontological Issues in Knowledge Sharing Google Scholar
  22. Simons, P. (1987). Parts, A Study in Ontology, 5–128, Oxford: Clarendon Press.Google Scholar
  23. van Heijst, G., Schreiber, A. T., and Wielinga, B. J. (1997). Using explicit ontologies in KBS development, International Journal of Human-Computer Studies, 45: 183–292.CrossRefGoogle Scholar
  24. Wielinga, B. J., Schreiber, A. T., and Breuker, J. A. (1992). KADS: a modelling approach to knowledge engineering, Knowledge Acquisition, Vol. 4:5–53.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  1. 1.Spanish Scientific Research Council (CSIC)MurciaSpain
  2. 2.Department of Social Science InformaticsUniversity of AmsterdamAmsterdamThe Netherlands
  3. 3.Department of Computing SciencesUniversity of MurciaMurciaSpain
  4. 4.Artificial Intelligence Research Institute (IIIA)Spanish Scientific Research Council (CSIC)Bellaterra, BarcelonaSpain

Personalised recommendations