Selecting Optimal Background Knowledge Sources for the Ontology Matching Task

Abdel Nasser Tigrine 1 Zohra Bellahsene 1 Konstantin Todorov 1
1 LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : It is a common practice to rely on background knowledge (BK) in order to assist and improve the ontology matching process. The choice of an appropriate source of background knowledge for a given matching task, however, remains a vastly unexplored question. In the current paper, we propose an automatic BK selection approach that does not depend on an initial direct matching, can handle multilingualism and is domain independent. The approach is based on the construction of an index for a set of BK candidates. The couple of ontologies to be aligned is modeled as a query with respect to the indexed BK sources and the best candidate is selected by following an information retrieval paradigm. We evaluate our system in a series of experiments in both general-purpose and domain-specific matching scenarios. The results show that our approach is capable of selecting the BK that provides the best alignment quality with respect to a given reference alignment for each of the considered matching tasks.
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Communication dans un congrès
EKAW: Knowledge Engineering and Knowledge Management, Nov 2016, Bologna, Italy. 20th International Conference on Knowledge Engineering and Knowledge Management, LNCS (10024), pp.651-665, 2016, Knowledge Engineering and Knowledge Management. 〈http://ekaw2016.cs.unibo.it/〉. 〈10.1007/978-3-319-49004-5_42〉
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Dernière modification le : jeudi 11 janvier 2018 - 06:14:31
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Abdel Nasser Tigrine, Zohra Bellahsene, Konstantin Todorov. Selecting Optimal Background Knowledge Sources for the Ontology Matching Task. EKAW: Knowledge Engineering and Knowledge Management, Nov 2016, Bologna, Italy. 20th International Conference on Knowledge Engineering and Knowledge Management, LNCS (10024), pp.651-665, 2016, Knowledge Engineering and Knowledge Management. 〈http://ekaw2016.cs.unibo.it/〉. 〈10.1007/978-3-319-49004-5_42〉. 〈lirmm-01407888〉

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