A semantic-based method for extracting concept definitions from scientific publications: evaluation in the autism phenotype domain

Journal of Biomedical Semantics

Amar K Das¹  Martin J O’Connor²  Saeed Hassanpour² 

[1] Geisel School of Medicine at Dartmouth, 46 Centerra Drive, Suite 330, Lebanon, NH 03766, USA;Stanford Center for Biomedical Informatics Research, Stanford, CA 94305, USA

关键词: Autism phenotypes;    Biomedical definitions;    Rules;    Ontologies;    Knowledge acquisition;   

Others  :  824733
DOI  :  10.1186/2041-1480-4-14

 received in 2012-09-29, accepted in 2013-07-04,  发布年份 2013

【 摘 要 】

Background

A variety of informatics approaches have been developed that use information retrieval, NLP and text-mining techniques to identify biomedical concepts and relations within scientific publications or their sentences. These approaches have not typically addressed the challenge of extracting more complex knowledge such as biomedical definitions. In our efforts to facilitate knowledge acquisition of rule-based definitions of autism phenotypes, we have developed a novel semantic-based text-mining approach that can automatically identify such definitions within text.

Results

Using an existing knowledge base of 156 autism phenotype definitions and an annotated corpus of 26 source articles containing such definitions, we evaluated and compared the average rank of correctly identified rule definition or corresponding rule template using both our semantic-based approach and a standard term-based approach. We examined three separate scenarios: (1) the snippet of text contained a definition already in the knowledge base; (2) the snippet contained an alternative definition for a concept in the knowledge base; and (3) the snippet contained a definition not in the knowledge base. Our semantic-based approach had a higher average rank than the term-based approach for each of the three scenarios (scenario 1: 3.8 vs. 5.0; scenario 2: 2.8 vs. 4.9; and scenario 3: 4.5 vs. 6.2), with each comparison significant at the p-value of 0.05 using the Wilcoxon signed-rank test.

Conclusions

Our work shows that leveraging existing domain knowledge in the information extraction of biomedical definitions significantly improves the correct identification of such knowledge within sentences. Our method can thus help researchers rapidly acquire knowledge about biomedical definitions that are specified and evolving within an ever-growing corpus of scientific publications.

【 授权许可】

   
2013 Hassanpour et al.; licensee BioMed Central Ltd.

【 图 表 】

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