Uncertainty in Ontology Matching: A Decision Rule-Based Approach

Abstract : Considering the high heterogeneity of the ontologies pub-lished on the web, ontology matching is a crucial issue whose aim is to establish links between an entity of a source ontology and one or several entities from a target ontology. Perfectible similarity measures, consid-ered as sources of information, are combined to establish these links. The theory of belief functions is a powerful mathematical tool for combining such uncertain information. In this paper, we introduce a decision pro-cess based on a distance measure to identify the best possible matching entities for a given source entity.
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Amira Essaid, Arnaud Martin, Grégory Smits, Boutheina Ben Yaghlane. Uncertainty in Ontology Matching: A Decision Rule-Based Approach. International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU), Jul 2014, Montpellier, France. pp.46 - 55, ⟨10.1007/978-3-319-08795-5_6⟩. ⟨hal-01108547⟩

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