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.
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. The increasing number of ontologies of the semantic web poses new challenges for ontology mapping. Ontology mapping in the context of question answering can provide more correct results if the mapping process can deal with uncertainty effectively that is caused by the incomplete and inconsistent information used and produced by the mapping process. We present a novel approach of how Dempster-Shafer belief functions can be used to represent uncertain similarities created by both syntactic and semantic similarity algorithms. For ontology mapping in the context of question answering on the semantic web we propose a multi agent framework where agents create dynamic ontology mappings in order to integrate information and provide precise answers for the users query. We also discuss the problems which can be encountered if we have conflicting beliefs between agents in a particular mapping.