title: Knowledge Extraction from MEDLINE by Combining Clustering with Natural Language Processing.
abstract: The identification of relevant predicates between co-occurring concepts in scientific literature databases like MEDLINE is crucial for using these sources for knowledge extraction, in order to obtain meaningful biomedical predications as subject-predicate-object triples. We consider the manually assigned MeSH indexing terms (main headings and subheadings) in MEDLINE records as a rich resource for extracting a broad range of domain knowledge. In this paper, we explore the combination of a clustering method for co-occurring concepts based on their related MeSH subheadings in MEDLINE with the use of SemRep, a natural language processing engine, which extracts predications from free text documents. As a result, we generated sets of clusters of co-occurring concepts and identified the most significant predicates for each cluster. The association of such predicates with the co-occurrences of the resulting clusters produces the list of predications, which were checked for relevance.
authors.affiliation: Institute of Medical Informatics, Statistics, and Documentation, Medical University of Graz, Austria.
authors.forename: Jose A
authors.initials: JA
authors.lastname: Miñarro-Giménez
authors.affiliation: Institute of Medical Informatics, Statistics, and Documentation, Medical University of Graz, Austria.
authors.forename: Markus
authors.initials: M
authors.lastname: Kreuzthaler
authors.affiliation: Institute of Medical Informatics, Statistics, and Documentation, Medical University of Graz, Austria.
authors.forename: Stefan
authors.initials: S
authors.lastname: Schulz
mesh_terms: D016000:Cluster Analysis; D016208:Databases, Factual; D006801:Humans; D016247:Information Storage and Retrieval; D016239:MEDLINE; D046650:Medical Subject Headings; D009323:Natural Language Processing; D012660:Semantics