A Study in Empirical and ‘Casuistic’ Analysis of Ontology Mapping Results

  • Ondřej Šváb
  • Vojtěch Svátek
  • Heiner Stuckenschmidt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4519)


Many ontology mapping systems nowadays exist. In order to evaluate their strengths and weaknesses, benchmark datasets (ontology collections) have been created, several of which have been used in the most recent edition of the Ontology Alignment Evaluation Initiative (OAEI). While most OAEI tracks rely on straightforward comparison of the results achieved by the mapping systems with some kind of reference mapping created a priori, the ’conference’ track (based on the OntoFarm collection of heterogeneous ’conference organisation’ ontologies) instead encompassed multiway manual as well as automated analysis of mapping results themselves, with ‘correct’ and ‘incorrect’ cases determined a posteriori. The manual analysis consisted in simple labelling of discovered mappings plus discussion of selected cases (‘casuistics’) within a face-to-face consensus building workshop. The automated analysis relied on two different tools: the DRAGO system for testing the consistency of aligned ontologies and the LISp-Miner system for discovering frequent associations in mapping meta-data including the phenomenon of graph-based mapping patterns. The results potentially provide specific feedback to the developers and users of mining tools, and generally indicate that automated mapping can rarely be successful without considering the larger context and possibly deeper semantics of the entities involved.


  1. 1.
    Bouquet, P., Giunchiglia, F., van Harmelen, F., Serafini, L., Stuckenschmidt, H.: C-OWL: Contextualizing Ontologies. In: Fensel, D., Sycara, K.P., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 164–179. Springer, Heidelberg (2003)Google Scholar
  2. 2.
    Ehrig, M., Staab, S., Sure, Y.: Bootstrapping Ontology Alignment Methods with APFEL. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 186–200. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  3. 3.
    Euzenat, J., Mochol, M., Shvaiko, P., Stuckenschmidt, H., Šváb, O., Svátek, V., van Hage, W.R., Yatskevich, M.: First Results of the Ontology Alignment Evaluation Initiative 2006. In: International Workshop on Ontology Matching collocated with the 5th International Semantic Web Conference ISWC-2006, GA Center, Athens, Georgia, USA, November 5 (2006)Google Scholar
  4. 4.
    Gangemi, A.: Ontology Design Patterns for Semantic Web Content. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 262–276. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Ghidini, C., Serafini, L.: Reconciling concepts and relations in heterogeneous ontologies. In: Sure, Y., Domingue, J. (eds.) ESWC 2006. LNCS, vol. 4011, pp. 50–64. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Kavalec, M., Svátek, V.: A Study on Automated Relation Labelling in Ontology Learning. In: Buitelaar, P., Cimiano, P., Magnini, B. (eds.) Ontology Learning and Population, pp. 44–58. IOS Press, Amsterdam (2005)Google Scholar
  7. 7.
    Meilicke, C., Stuckenschmidt, H., Tamilin, A.: Improving Automatically Created Mappings Using Logical Reasoning. In: Ontology Matching 2006, Workshop at ISWC’06 (2006)Google Scholar
  8. 8.
    Rauch, J., Šimunek, M.: An Alternative Approach to Mining Association Rules. In: Lin, T.Y., Ohsuga, S., Liau, C.J., Tsumoto, S. (eds.) Data Mining: Foundations, Methods, and Applications, pp. 211–232. Springer, Heidelberg (2005)Google Scholar
  9. 9.
    Rector, A.L., Drummond, N., Horridge, M., Rogers, J.D., Knublauch, H., Stevens, R., Wang, H., Wroe, C.: OWL Pizzas: Practical Experience of Teaching OWL-DL: Common Errors & Common Patterns. In: Motta, E., Shadbolt, N.R., Stutt, A., Gibbins, N. (eds.) EKAW 2004. LNCS (LNAI), vol. 3257, pp. 63–81. Springer, Heidelberg (2004)Google Scholar
  10. 10.
    Serafini, L., Tamilin, A.: DRAGO: Distributed Reasoning Architecture for the Semantic Web. In: Gómez-Pérez, A., Euzenat, J. (eds.) ESWC 2005. LNCS, vol. 3532, pp. 361–376. Springer, Heidelberg (2005)Google Scholar
  11. 11.
    Shvaiko, P., Euzenat, J.: A Survey of Schema-based Matching Approaches. Journal on Data Semantics (2005)Google Scholar
  12. 12.
    Šváb, O., Svátek, V., Berka, P., Rak, D., Tomášek, P.: OntoFarm: Towards an Experimental Collection of Parallel Ontologies. Poster Session at ISWC’05 (2005)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Ondřej Šváb
    • 1
  • Vojtěch Svátek
    • 1
  • Heiner Stuckenschmidt
    • 2
  1. 1.Department of Information and Knowledge Engineering, University of Economics, Prague, W. Churchill Sq. 4, 130 67 Praha 3Czech Republic
  2. 2.Universität Mannheim, Institut für Informatik, A5, 6 68159 MannheimGermany

Personalised recommendations