Advertisement

Analysing Ontological Structures through Name Pattern Tracking

  • Ondřej Šváb-Zamazal
  • Vojtěch Svátek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5268)

Abstract

Concept naming over the taxonomic structure is a useful indicator of the quality of design as well as source of information exploitable for various tasks such as ontology refactoring and mapping. We analysed collections of OWL ontologies with the aim of determining the frequency of several combined name&graph patterns potentially indicating underlying semantic structures. Such structures range from simple set-theoretic subsumption to more complex constructions such as parallel taxonomies of different entity types. The final goal is to help refactor legacy ontologies as well as to ease automatic alignment among different models. The results show that in most ontologies there is a significant number of occurrences of such patterns. Moreover, their detection even using very simple methods has precision sufficient for a semi-automated analysis scenario.

Keywords

Structural Cluster Head Noun Ontology Mapping Ontological Structure Ontology Match 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Baumeister, J., Seipel, D.: Smelly Owls – Design Anomalies in Ontologies. In: Proc. FLAIRS 2005, pp. 215–220 (2005); Again, as the token matching is considered positively, reflecting thesaurus correspondence would improve the recall of the pattern detection Google Scholar
  2. 2.
    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
  3. 3.
    Giunchiglia, F., Marchese, M., Zaihrayeu, I.: Encoding Classifications into Lightweight Ontologies. In: Sure, Y., Domingue, J. (eds.) ESWC 2006. LNCS, vol. 4011, pp. 80–94. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Hepp, M., de Bruijn, J.: GenTax: A Generic Methodology for Deriving OWL and RDF-S Ontologies from Hierarchical Classifications, Thesauri, and Inconsistent Taxonomies. In: Franconi, E., Kifer, M., May, W. (eds.) ESWC 2007. LNCS, vol. 4519, pp. 129–144. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Kavalec, M., Svátek, V.: Information Extraction and Ontology Learning Guided by Web Directory. In: ECAI Workshop on NLP and ML for ontology engineering, Lyon (2002)Google Scholar
  6. 6.
    Magnini, B., Serafini, L., Speranza, M.: Making Explicit the Hidden Semantics of Hierarchical Classifications. In: Proc. AI*IA 2003 (2003)Google Scholar
  7. 7.
    Meilicke, C., Völker, J., Stuckenschmidt, H.: Learning Disjointness for Debugging Mappings between Lightweight Ontologies. In: Gangemi, A., Euzenat, J. (eds.) EKAW 2008. LNCS(LNAI), vol. 5268, pp. 93–108. Springer, Heidelberg (2008)Google Scholar
  8. 8.
    Nirenburg, S., Wilks, Y.: Whats in a symbol: Ontology and the surface of language. Journal of Experimental and Theoretical AI 13, 9–23Google Scholar
  9. 9.
    Rector, A. (ed.): Representing Specified Values in OWL: ”value partitions” and ”value sets”. W3C Working Group Note (May 17, 2005), http://www.w3.org/TR/swbp-specified-values/
  10. 10.
    Scharffe, F., Euzenat, J., Ding, Y., Fensel, D.: Correspondence Patterns for Ontology Mediation. In: Workshop on Ontology Matching collocated with ISWC, Busan, Korea (2007)Google Scholar
  11. 11.
    Serafini, L., Zanobini, S., Sceffer, S., Bouquet, P.: Matching Hierarchical Classifications with Attributes. In: Sure, Y., Domingue, J. (eds.) ESWC 2006. LNCS, vol. 4011, pp. 4–18. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  12. 12.
    Svátek, V.: Design Patterns for Semantic Web Ontologies: Motivation and Discussion. In: 7th Conf. on Business Information Systems (BIS 2004), Poznan (April 2004)Google Scholar
  13. 13.
    Svátek, V., Šváb, O.: Tracking Name Patterns in OWL Ontologies. In: EON-2007 at ISWC-2007, Busan, Korea (2007)Google Scholar
  14. 14.
    Šváb, O., Svátek, V., Stuckenschmidt, H.: Study in Empirical and ’Casuistic’ Analysis of Ontology Mapping Results. In: Franconi, E., Kifer, M., May, W. (eds.) ESWC 2007. LNCS, vol. 4519. Springer, Heidelberg (2007)Google Scholar
  15. 15.
    Tempich, C., Volz, R.: Towards a benchmark for Semantic Web reasoners - an analysis of the DAML ontology library. In: EON Workshop at ISWC (2003)Google Scholar
  16. 16.
    Vrandecic, D., Sure, Y.: How to Design Better Ontology Metrics. In: Franconi, E., Kifer, M., May, W. (eds.) ESWC 2007. LNCS, vol. 4519, pp. 311–325. Springer, Heidelberg (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Ondřej Šváb-Zamazal
    • 1
  • Vojtěch Svátek
    • 1
  1. 1.Dept. Information and Knowledge EngineeringUniversity of Economics, PraguePragueCzech Republic

Personalised recommendations