Leveraging data and structure in ontology integration

TLDR

Ontology integration research has focused on logical constraints and data instance matching, but few studies exploit both data and structure together. The authors propose ILIADS, an algorithm that tightly integrates data matching with logical reasoning to improve ontology matching. ILIADS was evaluated on 30 OWL Lite ontology pairs using human‑reviewed matchings and benchmarked against FCA‑merge and COMA++. ILIADS achieved a 25% quality improvement over FCA‑merge and an 11% recall improvement over COMA++.

Abstract

There is a great deal of research on ontology integration which makes use of rich logical constraints to reason about the structural and logical alignment of ontologies. There is also considerable work on matching data instances from heterogeneous schema or ontologies. However, little work exploits the fact that ontologies include both data and structure. We aim to close this gap by presenting a new algorithm (ILIADS) that tightly integrates both data matching and logical reasoning to achieve better matching of ontologies. We evaluate our algorithm on a set of 30 pairs of OWL Lite ontologies with the schema and data matchings found by human reviewers. We compare against two systems - the ontology matching tool FCA-merge [28] and the schema matching tool COMA++ [1]. ILIADS shows an average improvement of 25% in quality over FCA-merge and a 11% improvement in recall over COMA++.