Lexical Enrichment of a Human Anatomy Ontology using WordNet

Abstract. This paper is concerned with lexical enrichment of ontologies, i.e. how to enrich a given ontology with lexical entries derived from a semantic lexicon. We present an approach towards the integration of both types of resources, in particular for the human anatomy domain as represented by the Foundational Model of Anatomy (FMA). The paper describes our approach on combining the FMA with WordNet by use of a simple algorithm for domain-specific word sense disambiguation, which selects the most likely sense for an FMA term by computing statistical significance of synsets on a corpus of Wikipedia pages on human anatomy. The approach is evaluated on a benchmark of 50 ambiguous FMA terms with manually assigned WordNet synsets (i.e. senses).