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Learning Class Disjointness Axioms Using Grammatical Evolution

  • Thu Huong NguyenEmail author
  • Andrea G. B. Tettamanzi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11451)

Abstract

Today, with the development of the Semantic Web, Linked Open Data (LOD), expressed using the Resource Description Framework (RDF), has reached the status of “big data” and can be considered as a giant data resource from which knowledge can be discovered. The process of learning knowledge defined in terms of OWL 2 axioms from the RDF datasets can be viewed as a special case of knowledge discovery from data or “data mining”, which can be called“RDF mining”. The approaches to automated generation of the axioms from recorded RDF facts on the Web may be regarded as a case of inductive reasoning and ontology learning. The instances, represented by RDF triples, play the role of specific observations, from which axioms can be extracted by generalization. Based on the insight that discovering new knowledge is essentially an evolutionary process, whereby hypotheses are generated by some heuristic mechanism and then tested against the available evidence, so that only the best hypotheses survive, we propose the use of Grammatical Evolution, one type of evolutionary algorithm, for mining disjointness OWL 2 axioms from an RDF data repository such as DBpedia. For the evaluation of candidate axioms against the DBpedia dataset, we adopt an approach based on possibility theory.

Keywords

Ontology learning OWL 2 axiom Grammatical evolution 

References

  1. 1.
    Guarino, N., Oberle, D., Staab, S.: What is an ontology? In: Staab, S., Studer, R. (eds.) Handbook on Ontologies. IHIS, pp. 1–17. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-540-92673-3_0CrossRefGoogle Scholar
  2. 2.
    Maedche, A., Staab, S.: Ontology learning for the semantic web. IEEE Intell. Syst. 16(2), 72–79 (2001)CrossRefGoogle Scholar
  3. 3.
    Lehmann, J., Völker, J.: Perspectives on Ontology Learning, Studies on the Semantic Web, vol. 18. IOS Press, Amsterdam (2014)Google Scholar
  4. 4.
    Drumond, L., Girardi, R.: A survey of ontology learning procedures. In: WONTO. CEUR Workshop Proceedings, vol. 427. CEUR-WS.org (2008)Google Scholar
  5. 5.
    Hazman, M., El-Beltagy, S.R., Rafea, A.: Article: a survey of ontology learning approaches. Int. J. Comput. Appl. 22(8), 36–43 (2011)Google Scholar
  6. 6.
    Zhao, L., Ichise, R.: Mid-ontology learning from linked data. In: Pan, J.Z., et al. (eds.) JIST 2011. LNCS, vol. 7185, pp. 112–127. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-29923-0_8CrossRefGoogle Scholar
  7. 7.
    Tiddi, I., Mustapha, N.B., Vanrompay, Y., Aufaure, M.-A.: Ontology learning from open linked data and web snippets. In: Herrero, P., Panetto, H., Meersman, R., Dillon, T. (eds.) OTM 2012. LNCS, vol. 7567, pp. 434–443. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-33618-8_59CrossRefGoogle Scholar
  8. 8.
    Zhu, M.: DC proposal: ontology learning from noisy linked data. In: Aroyo, L., et al. (eds.) ISWC 2011. LNCS, vol. 7032, pp. 373–380. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-25093-4_31CrossRefGoogle Scholar
  9. 9.
    Bühmann, L., Lehmann, J.: Universal OWL axiom enrichment for large knowledge bases. In: ten Teije, A., et al. (eds.) EKAW 2012. LNCS (LNAI), vol. 7603, pp. 57–71. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-33876-2_8CrossRefGoogle Scholar
  10. 10.
    Völker, J., Fleischhacker, D., Stuckenschmidt, H.: Automatic acquisition of class disjointness. J. Web Sem. 35, 124–139 (2015)CrossRefGoogle Scholar
  11. 11.
    Lehmann, J.: Dl-learner: learning concepts in description logics. J. Mach. Learn. Res. 10, 2639–2642 (2009)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Völker, J., Vrandečić, D., Sure, Y., Hotho, A.: Learning disjointness. In: Franconi, E., Kifer, M., May, W. (eds.) ESWC 2007. LNCS, vol. 4519, pp. 175–189. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-72667-8_14CrossRefGoogle Scholar
  13. 13.
    Fleischhacker, D., Völker, J.: Inductive learning of disjointness axioms. In: Meersman, R., et al. (eds.) OTM 2011. LNCS, vol. 7045, pp. 680–697. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-25106-1_20CrossRefGoogle Scholar
  14. 14.
    Bühmann, L., Lehmann, J.: Pattern based knowledge base enrichment. In: Alani, H., et al. (eds.) ISWC 2013. LNCS, vol. 8218, pp. 33–48. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-41335-3_3CrossRefGoogle Scholar
  15. 15.
    O’Neill, M., Ryan, C.: Grammatical evolution. Trans. Evol. Comput. 5(4), 349–358 (2001).  https://doi.org/10.1109/4235.942529CrossRefGoogle Scholar
  16. 16.
    Dempsey, I., O’Neill, M., Brabazon, A.: Foundations in Grammatical Evolution for Dynamic Environments - Chapter 2 Grammatical Evolution. Studies in Computational Intelligence, vol. 194. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-00314-1CrossRefGoogle Scholar
  17. 17.
    Ryan, C., Collins, J.J., Neill, M.O.: Grammatical evolution: evolving programs for an arbitrary language. In: Banzhaf, W., Poli, R., Schoenauer, M., Fogarty, T.C. (eds.) EuroGP 1998. LNCS, vol. 1391, pp. 83–96. Springer, Heidelberg (1998).  https://doi.org/10.1007/BFb0055930CrossRefGoogle Scholar
  18. 18.
    Mahfoud, S.W.: Crowding and preselection revisited. In: PPSN, pp. 27–36. Elsevier (1992)Google Scholar
  19. 19.
    Tettamanzi, A.G.B., Faron-Zucker, C., Gandon, F.: Testing OWL axioms against RDF facts: a possibilistic approach. In: Janowicz, K., Schlobach, S., Lambrix, P., Hyvönen, E. (eds.) EKAW 2014. LNCS (LNAI), vol. 8876, pp. 519–530. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-13704-9_39CrossRefGoogle Scholar
  20. 20.
    Zadeh, L.A.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst. 1, 3–28 (1978)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Université Côte d’Azur, CNRS, Inria, I3SNiceFrance

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