Rerendering Semantic Ontologies: Automatic Extensions to UMLS through Corpus Analytics

Abstract

In this paper, we discuss the utility and deficiencies of existing ontology resources for a number of language processing applications. We describe a technique for increasing the semantic type coverage of a specific ontology, the National Library of Medicine's UMLS, with the use of robust finite state methods used in conjunction with large-scale corpus analytics of the domain corpus. We call this technique "semantic rerendering" of the ontology. This research has been done in the context of Medstract, a joint Brandeis-Tufts effort aimed at developing tools for analyzing biomedical language (i.e., Medline), as well as creating targeted databases of bio-entities, biological relations, and pathway data for biological researchers. Motivating the current research is the need to have robust and reliable semantic typing of syntactic elements in the Medline corpus, in order to improve the overall performance of the information extraction applications mentioned above.