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Decision Making for Ontology Matching under the Theory of Belief Functions

Abstract : Ontology matching is a solution to mitigate the effect of semantic heterogeneity. Matching techniques, based on similarity measures, are used to find correspondences between ontologies. Using a unique similarity measure does not guarantee a perfect alignment. For that reason, it is necessary to use more than a similarity measure to take advantage of features of each one and then to combine the different outcomes. In this thesis, we propose a credibilistic decision process by using the theory of belief functions. First, we model the alignments, obtained after a matching process, under the theory of belief functions. Then, we combine the different outcomes through using adequate combination rules. Due to our awareness that making decision is a crucial step in any process and that most of the decision rules of the belief function theory are able to give results on a unique element, we propose a decision rule based on a distance measure able to make decision on union of elements (i.e. to identify for each source entity its corresponding target entities).
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https://hal.archives-ouvertes.fr/tel-02501458
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  • HAL Id : tel-02501458, version 1

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Amira Essaid. Decision Making for Ontology Matching under the Theory of Belief Functions. Artificial Intelligence [cs.AI]. Université de Rennes 1 [UR1]; Université de Tunis, 2015. English. ⟨tel-02501458⟩

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