Ontology-Driven Semantic Ranking for Natural Language Disambiguation in the OntoNL Framework

  • Anastasia Karanastasi
  • Stavros Christodoulakis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4519)


The measurement of the semantic relatedness has many applications in natural language processing, and many different measures have been proposed. Most of these measures use WordNet as their central resource and not domain ontologies of a particular context. We propose and evaluate a semantic relatedness measure for OWL domain ontologies that concludes to the semantic ranking of ontological, grammatically-related structures. This procedure is used to disambiguate in a particular domain of context and represent in an ontology query language, natural language expressions. The ontology query language that we use is the SPARQL. The construction of the queries is automated and also dependent on the semantic relatedness measurement of ontology concepts. The methodology has been successfully integrated into the OntoNL Framework, a natural language interface generator for knowledge repositories. The experimentations show a good performance in a number of OWL ontologies.


natural language interfaces ontologies semantic relatedness query representation 


  1. 1.
    Budanitsky, A., Hirst, G.: Evaluating WordNet-based Measures of Lexical Semantic Relatedness. Computational Linguistics 32(1), 13–47 (2006)CrossRefGoogle Scholar
  2. 2.
    Fellbaum, C. (ed.): WordNet: An Electronic Lexical Database. The MIT Press, Cambridge (1998)zbMATHGoogle Scholar
  3. 3.
    Karanastasi, A., Zwtos, A., Christodoulakis, S.: User Interactions with Multimedia Repositories using Natural Language Interfaces - OntoNL: an Architectural Framework and its Implementation. Journal of Digital Information Management - JDIM 4(4) (2006)Google Scholar
  4. 4.
    Kozima, H., Furugori, T.: Similarity between words computed by spreading activation on an English dictionary. In: Proceedings of 6th Conference of the European Chapter of the Association for Computational Linguistics (EACL-93), Utrecht, pp. 232–239 (1993)Google Scholar
  5. 5.
    Leacock, C., Chodorow, M.: Combining local context and WordNet similarity for word sense identification. In: Fellbaum, C. (ed.) WordNet: An Electronic Lexical Database, pp. 265–283. The MIT Press, Cambridge (1998)Google Scholar
  6. 6.
    Lin, D.: An information-theoretic definition of similarity. In: Proceedings of the 15th International Conference on Machine Learning (1998)Google Scholar
  7. 7.
    Rada, R., Bicknell, E.: Ranking documents with a thesaurus. JASIS 40(5), 304–310 (1989)CrossRefGoogle Scholar
  8. 8.
    Rada, R., Mili, H., Bicknell, E., Blettner, M.: Development and application of a metric on semantic nets. IEEE Transactions on Systems, Man, and Cybernetics 19(1), 17–30 (1989)CrossRefGoogle Scholar
  9. 9.
    Resnik, P.: Using information content to evaluate semantic similarity. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, Montreal, Canada, pp. 448–453 (1995)Google Scholar
  10. 10.
    Sussna, M.: Word sense disambiguation for free-text indexing using a massive semantic network. In: Proceedings of the Second International Conference on Information and Knowledge Management (CIKM-93), Arlington, Virginia, pp. 67–74 (1993)Google Scholar
  11. 11.
    Wu, Z., Palmer, M.: Verb semantics and lexical selection. In: Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics, Las Cruces, New Mexico, pp. 133–138 (1994)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Anastasia Karanastasi
    • 1
  • Stavros Christodoulakis
    • 1
  1. 1.Laboratory of Distributed Multimedia Information Systems / Technical University of Crete (MUSIC/TUC)University Campus, Kounoupidiana, ChaniaGreece

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