Multi-level Networked Knowledge Base: DDL-Reasoning

Abstract : This paper describes a new formalism based on multi-level networked knowledge (MLNK), a combination of different ontologies describing heterogeneous and complementary domains aligned with semantic correspondences. Ontology alignments make explicit the correspondences between terms from different ontologies and must be taken into account in reasoning, where two explicit form of correspondences are given: mappings represent predefined relations such as subsumption, equivalence, or disjointness, that have a fixed semantics in all interpretations; as well as links that can relate complementary ontologies by introducing terms defined by experts, and their semantics varies according to interpretations. The proposed MLNK formalism can be transformed into a Distributed System capable of supporting DDL semantics. It permits to apply a contextual reasoning where ontologies and alignments by pairs of ontologies are developed in different and incompatible contexts. The semantic of the proposed formalism is extensively described along with an illustrative example.
Type de document :
Communication dans un congrès
Ladjel Bellatreche; Óscar Pastor; Jesús M. Almendros Jiménez; Yamine Aït-Ameur. 6th International Conference, MEDI 2016, Sep 2016, Almería, Spain. Springer International Publishing, Model and Data Engineering, Volume 9893, pp 118-131, 2016, Lecture Notes in Computer Science. 〈10.1007/978-3-319-45547-1_10〉
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https://hal-emse.ccsd.cnrs.fr/emse-01364941
Contributeur : Florent Breuil <>
Soumis le : mardi 13 septembre 2016 - 10:07:30
Dernière modification le : dimanche 15 octobre 2017 - 22:44:06

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Sihem Klai, Antoine Zimmermann, Mohamed Tarek Khadir. Multi-level Networked Knowledge Base: DDL-Reasoning. Ladjel Bellatreche; Óscar Pastor; Jesús M. Almendros Jiménez; Yamine Aït-Ameur. 6th International Conference, MEDI 2016, Sep 2016, Almería, Spain. Springer International Publishing, Model and Data Engineering, Volume 9893, pp 118-131, 2016, Lecture Notes in Computer Science. 〈10.1007/978-3-319-45547-1_10〉. 〈emse-01364941〉

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