Using a search engine-based mutually reinforcing approach to assess the semantic relatedness of biomedical terms

Yi Yu Hsu, Hung Yu Chen, Hung Yu Kao

研究成果: Article同行評審

2 引文 斯高帕斯(Scopus)

摘要

Background: Determining the semantic relatedness of two biomedical terms is an important task for many text-mining applications in the biomedical field. Previous studies, such as those using ontology-based and corpus-based approaches, measured semantic relatedness by using information from the structure of biomedical literature, but these methods are limited by the small size of training resources. To increase the size of training datasets, the outputs of search engines have been used extensively to analyze the lexical patterns of biomedical terms. Methodology/Principal Findings: In this work, we propose the Mutually Reinforcing Lexical Pattern Ranking (ReLPR) algorithm for learning and exploring the lexical patterns of synonym pairs in biomedical text. ReLPR employs lexical patterns and their pattern containers to assess the semantic relatedness of biomedical terms. By combining sentence structures and the linking activities between containers and lexical patterns, our algorithm can explore the correlation between two biomedical terms. Conclusions/ Significance: The average correlation coefficient of the ReLPR algorithm was 0.82 for various datasets. The results of the ReLPR algorithm were significantly superior to those of previous methods.

原文English
文章編號e77868
期刊PloS one
8
發行號11
DOIs
出版狀態Published - 2013 11月 13

All Science Journal Classification (ASJC) codes

  • 多學科

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