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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 struc ture. 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 and the schema matching tool COMA++. ILIADS shows an average improvement of 25% in quality over FCA-merge and a 11% improvement in recall over COMA++. To better understand how ontology alignment algorithms behave, we have developed HOMER, a GUI for ontology visualization and analysis. HOMER grants the user fine-grained control of the alignment algorithm, displaying its decision step-by-step and allowing the user to override them. Furthermore, one can also compare different instances of the same algorithm (with different parameters) or separate algorithms. This work was supported by the National Science Foundation, NSF #0438866.


Octavian Udrea, Lise Getoor, Renee Miller


  • Octavian Udrea and Lise Getoor. Combining Statistical and Logical In¬ference for Ontology Alignment. In IJCAI-07 Workshop on Semantic Web for Collaborative Knowledge Acquisition, 2007 [PDF].
  • Octavian Udrea, Lise Getoor, and Renee J. Miller. Leveraging Data and Structure in Ontology Integration. In SIGMOD, pages 449–460, 2007 [PDF].
  • Octavian Udrea, Lise Getoor, and Renee J. Miller. HOMER: Ontology alignment visualization and analysis. In SIGMOD, pages 449–460, 2007 [PDF][Poster][Short demo]


  • ILIADS presented at SIGMOD'07 [PPT]
Last updated: March 26 2008
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