Using contextual and lexical features to restructure and validate the classification of biomedical concepts

Background: Biomedical ontologies are critical for integration of data from diverse sources and for use by knowledge-based biomedical applications, especially natural language processing as well as associated mining and reasoning systems. The effectiveness of these systems is heavily dependent on the quality of the ontological terms and their classifications. To assist in developing and maintaining the ontologies objectively, we propose automatic approaches to classify and/or validate their semantic categories. In previous work, we developed an approach using contextual syntactic features obtained from a large domain corpus to reclassify and validate concepts of the Unified Medical Language System (UMLS), a comprehensive resource of biomedical terminology. In this paper, we introduce another classification approach based on words of the concept strings and compare it to the contextual syntactic approach. Results: The string-based approach achieved an error rate of 0.143, with a mean r...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Jung-Wei Fan, Hua Xu, Carol Friedman
• Keywords: BMCBI 2007, Contextual Syntactic, Error Rate, Mean Reciprocal Rank