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Automatic Ontology Learning from Heterogeneous Relational Databases: Application in Alimentation Risks Field

Abstract : In this paper, we propose a semantic approach for automatic ontology learning from heterogeneous relational databases in order to facilitate their integration. The semantic enrichment of heterogeneous databases, which cover the same domain, is essential to integrate them. Our approach is based on Wordnet and Wup’s measure for measuring the semantic similarity between elements of these databases. It is described by a detailed process that can allow not only the generation of ontology but also its evolution as the evolution of its databases. We applied our approach in the alimentation risks field that is characterized by a large number of scientific databases. The developed prototype has been compared with similar tools of generation ontology from databases. The result confirms the quality of our prototype that returns the generic ontology from many relational databases.
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Submitted on : Wednesday, November 7, 2018 - 9:33:03 AM
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Aicha Aggoune. Automatic Ontology Learning from Heterogeneous Relational Databases: Application in Alimentation Risks Field. 6th IFIP International Conference on Computational Intelligence and Its Applications (CIIA), May 2018, Oran, Algeria. pp.199-210, ⟨10.1007/978-3-319-89743-1_18⟩. ⟨hal-01913894⟩

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