title: Natural Language Processing methods and systems for biomedical ontology learning.
abstract: While the biomedical informatics community widely acknowledges the utility of domain ontologies, there remain many barriers to their effective use. One important requirement of domain ontologies is that they must achieve a high degree of coverage of the domain concepts and concept relationships. However, the development of these ontologies is typically a manual, time-consuming, and often error-prone process. Limited resources result in missing concepts and relationships as well as difficulty in updating the ontology as knowledge changes. Methodologies developed in the fields of Natural Language Processing, information extraction, information retrieval and machine learning provide techniques for automating the enrichment of an ontology from free-text documents. In this article, we review existing methodologies and developed systems, and discuss how existing methods can benefit the development of biomedical ontologies.
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh School of Medicine, PA 15232, USA.
authors.forename: Kaihong
authors.initials: K
authors.lastname: Liu
authors.forename: William R
authors.initials: WR
authors.lastname: Hogan
authors.forename: Rebecca S
authors.initials: RS
authors.lastname: Crowley
mesh_terms: D035843:Biomedical Research; D019295:Computational Biology; D057225:Data Mining / Q000379:methods*; D009323:Natural Language Processing; D018875:Vocabulary, Controlled