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.
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References
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_0
Maedche, A., Staab, S.: Ontology learning for the semantic web. IEEE Intell. Syst. 16(2), 72–79 (2001)
Lehmann, J., Völker, J.: Perspectives on Ontology Learning, Studies on the Semantic Web, vol. 18. IOS Press, Amsterdam (2014)
Drumond, L., Girardi, R.: A survey of ontology learning procedures. In: WONTO. CEUR Workshop Proceedings, vol. 427. CEUR-WS.org (2008)
Hazman, M., El-Beltagy, S.R., Rafea, A.: Article: a survey of ontology learning approaches. Int. J. Comput. Appl. 22(8), 36–43 (2011)
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_8
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_59
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_31
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_8
Völker, J., Fleischhacker, D., Stuckenschmidt, H.: Automatic acquisition of class disjointness. J. Web Sem. 35, 124–139 (2015)
Lehmann, J.: Dl-learner: learning concepts in description logics. J. Mach. Learn. Res. 10, 2639–2642 (2009)
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_14
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_20
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_3
O’Neill, M., Ryan, C.: Grammatical evolution. Trans. Evol. Comput. 5(4), 349–358 (2001). https://doi.org/10.1109/4235.942529
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/10.1007/978-3-642-00314-1
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/BFb0055930
Mahfoud, S.W.: Crowding and preselection revisited. In: PPSN, pp. 27–36. Elsevier (1992)
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_39
Zadeh, L.A.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst. 1, 3–28 (1978)
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Nguyen, T.H., Tettamanzi, A.G.B. (2019). Learning Class Disjointness Axioms Using Grammatical Evolution. In: Sekanina, L., Hu, T., Lourenço, N., Richter, H., García-Sánchez, P. (eds) Genetic Programming. EuroGP 2019. Lecture Notes in Computer Science(), vol 11451. Springer, Cham. https://doi.org/10.1007/978-3-030-16670-0_18
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