Unified Fuzzy Ontology Matching (UFOM) is an ontology matching system designed to discover semantic links between large real-world ontologies populated with entities from heterogeneous sources. In such ontologies, several entities in different ontologies are expected to be related to each other but not necessarily with one of the typical well-defined correspondence relationships (equivalent-to, subsumed-by). In particular, we define a new kind of correspondence relation called Relevance that reflects the relation between entities when they share a certain amount of mutual information. UFOM uses fuzzy set theory as a general framework for fuzzy ontology alignment. The framework enables representation of multiple types of correspondence relations and characterization of the uncertainty in the correspondence discovery process. UFOM computes multiple measures of similarity among ontology entities - syntactic, semantic, and structural. These measures are composed in a principled manner for ontology alignment. The system is evaluated using publicly available ontologies provided by Ontology Alignment Evaluation Initiative (OAEI). The performance of the proposed system is comparable to the top performing ontology matchers in OAEI. We also evaluate the UFOM system on a dataset from an enterprise application domain.
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