Uncertainty in Ontology Matching: A Decision Rule-Based Approach
This addresses the challenge of high heterogeneity in web ontologies for researchers and practitioners in semantic web and data integration, but it appears incremental as it builds on existing belief function methods.
The paper tackles the problem of uncertainty in ontology matching by introducing a decision process based on a distance measure to identify the best matching entities, using the theory of belief functions to combine imperfect similarity measures.
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.