Knowledge Extraction from MEDLINE by Combining Clustering with Natural Language Processing

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 ass...

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Bibliographic Details
Published in:AMIA ... Annual Symposium proceedings 2015, Vol.2015, p.915-924
Main Authors: Miñarro-Giménez, Jose A, Kreuzthaler, Markus, Schulz, Stefan
Format: Article
Language:English
Subjects:
Cluster Analysis
Databases, Factual
Humans
Information Storage and Retrieval
Medical Subject Headings
MEDLINE
Natural Language Processing
Semantics
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Summary: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.
ISSN:1559-4076