Uncertainty Handling in the Context of Ontology Mapping for Question-Answering

Miklos Nagy, Maria Vargas-Vera , Enrico Motta

This paper describes a framework for integrating similarity measures and Dempster-Shafer belief functions for data integration in the context of multi agent ontology mapping. In order to incorporate uncertainty inherent to the ontology mapping process, we propose utilizing the Dempster-Shafer model for dealing with incomplete and uncertain information produced during the mapping. A novel approach is presented how assessing belief can influence the similarities originally created by both syntactic and semantic similarity algorithms. Our approach is an alternative to the classical Bayesian reasoning which has been investigated for improving the efficiency of creating ontology mappings.

Subjects: 7.1 Multi-Agent Systems; 7. Distributed AI

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