Alignment Results of SOBOM for OAEI 2009

In this paper we give a brief explanation of how Anchor Concept and Sub-Ontology based Ontology Matching (SOBOM) gets the alignment results at OAEI2009. SOBOM deal with the ontology from two different views: an ontology with is-a hierarchical structure ' O and an ontology with other relationships ' ' O . Firstly, from the ' O view, SOBOM starts with a set of anchor concepts provided by linguistic matcher. And then it extracts subontologies based on the anchor concepts and ranks these sub-ontologies according to their depth. Secondly, SOBOM utilizes Semantic Inductive Similarity Flooding algorithm to compute the similarity of the concepts between the sub-ontologies derived from the two ontologies according the depth of sub-ontologies to get concept alignments. Finally, from the ' ' O view, SOBOM gets relationship alignments by using the concept alignment results in ' ' O . The experiment results show SOBOM can find more alignment results than other compared relevant methods with high degree of precision. 1 System presentation Currently more and more ontologies are distributedly built and used by different organizations. And these ontologies are usually light-weighted [1] containing lots of concepts especially in biomedicine, such as anatomy taxonomy NCI thesaurus. The Anchor Concept and Sub-ontology based Ontology Matching (SOBOM) is designed for matching light-weight ontologies. It handles an ontology from two v iews: ' O and ' ' O that are depicted in Fig. 1. The unique feature of our method is combin ing sub-ontology extract ion with ontology matching. 1.1 State, purpose, general statement SOBOM is an automat ic ontology matching tool. There are three matchers implemented in current version: linguistic matcher I-Sub [2], structure matcher SISF (Semantic Inductive Similarity Flooding) which was inspired by Anchor-Prompt [3] and SF [4] algorithms, and relat ionship matcher R-matcher which utilizes the results of SISF to get relat ionship alignments. In addit ion, a Sub-ontology Extractor (SoE) is integrated into SOBOM to ext ract sub-ontologies according to the result of ISub and rank them. The method of SOBOM is fully sequential, so it does not care how to combine the results of different matchers. The overview of the approach is illustrated in Fig. 2.