Multi-level networked knowledge: representation and DL-reasoning

Abstract : Integrating pre-existing, heterogeneous, and complementary ontologies, and exploiting them jointly in reasoning remains a major challenge. Ontology alignments make explicit the correspondences between terms from different ontologies and must be taken into account in reasoning. Two forms of correspondences can be introduced: mappings represent predefined relations such as subsumption, equivalence, or disjointness, that have a fixed semantics in all interpretations; links can relate complementary ontologies by introducing terms defined by experts, and their semantics varies according to interpretations. Different experts can introduce different terms according to their points of view, which brings semantically heterogeneous links. Thus, integrating pre-existing networks of aligned ontologies requires aligning terminologies from different alignments, so as to form higher level alignments. This generates networked knowledge that can in turn be aligned with other networked knowledge. As a result, we talk of multi-level networked knowledge, a concept that we formalise here and for which we propose a possible formal semantic for automating reasoning tasks. This semantic consists of reducing reasoning on networked knowledge to reasoning over DL formalisms for which we have reasoning procedures. The proposed approach is implemented and tested in order to compare results, for different networks.
Type de document :
Article dans une revue
International Journal of Metadata, Semantics and Ontologies, Inderscience, 2016, 11 (1), pp. 1-15. 〈〉. 〈10.1504/IJMSO.2016.078101〉
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Contributeur : Florent Breuil <>
Soumis le : jeudi 11 août 2016 - 10:32:48
Dernière modification le : mardi 23 octobre 2018 - 14:36:09



Sihem Klai, Antoine Zimmermann, Mohamed Tarek Khadir. Multi-level networked knowledge: representation and DL-reasoning. International Journal of Metadata, Semantics and Ontologies, Inderscience, 2016, 11 (1), pp. 1-15. 〈〉. 〈10.1504/IJMSO.2016.078101〉. 〈emse-01353342〉



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