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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Uncertainty in Ontology Matching: A Decision Rule-Based Approach
verfasst von : Amira Essaid, Arnaud Martin, Grégory Smits, Boutheina Ben Yaghlane
Erschienen in: Information Processing and Management of Uncertainty in Knowledge-Based Systems
Verlag: Springer International Publishing
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Considering the high heterogeneity of the ontologies published 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, considered 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 process based on a distance measure to identify the best possible matching entities for a given source entity.