A Coupled Statistical/Semantic Framework for Merging Heterogeneous Domain-Specific Ontologies

Discovering semantic correspondences between ontology elements is a crucial task for merging heterogeneous ontologies. Most ontology merging tools use several methods to aggregate and combine similarity measures. In addition, some of the ontology merging systems exploit external resources such as, Linguistic Knowledge Bases (e.g. WordNet) to support this task. However, the quality of their results is subjected to the limitations of the exploited knowledge base. In this paper, we present a framework that exploits multiple knowledge bases that cover information in multiple domains for: i) Indentifying and correcting incorrect semantic relations between the concepts of domain-specific ontologies. This is a primary step before ontology merging; ii) Merging domain-specific ontologies; and iii) Handling the issue of missing background knowledge in the exploited knowledge bases by utilizing statistical techniques. An experimental instantiation of the framework and comparisons with state-of-the-art syntactic and semantic-based systems validate our proposal.