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.
In @cite_12 , the author viewed ontology matching as a decision making process that must be handled under uncertainty. He presented a generic framework that uses Dempster-Shafer theory as a mathematical model for representing uncertain mappings as well as combining the results of the different matchers. Given two ontologies @math and @math , the frame of discernment represents the Cartesian product e x @math where each hypothesis is the couple @math such as e @math and @math . Each matcher is considered as an expert that returns a similarity measure converted into a basic belief mass. The Dempster rule of combination is used to combine the results provided by a matcher. The pairs with plausibility and belief below a given threshold are discarded. The remaining pairs represent the best mapping for a given entity.
@cite_12
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Ontologies, at least in the form of taxonomies, have proved rather successful, and are employed in many fields, as far apart as biology and finance. Reaching an agreement over a single ontology has proved difficult, and to obtain actual interoperability it is necessary to map the different ontologies. Mapping one entity between a source ontology and one in a target ontology means to compare the first entity with all the entities in the second ontology: matchers analyse different aspects of the entities to identify the similarities. A single matcher can analyse only some aspects, and often has to rely on uncertain information. Therefore combining the outcomes of different matchers can yield better results. In this paper I present a framework that uses Dempster-Shafer as a model for interpreting and combining results computed by the matchers.