PubMed smarter: Query expansion with implicit words based on gene ontology
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摘要
The biomedical literature is increasing rapidly, but most information retrieval systems for biomedicine are not what we really expect. In general, users suffer from exactly specifying what they want to the information retrieval systems, thereby getting back unsatisfied results from these systems. In this paper, we proposed PubMed Smarter that improves the effectiveness of information retrieval in PubMed. We built the word-relationship tree for biomedicine used to find implicit words. The implicit words are the ones correlative to a user query, and facilitate searching the PubMed database. Finally, we also used a fair assessment to evaluate the effectiveness of the system.
论文关键词:Text mining,Gene ontology,TF,Term Frequency,IDF,Inverse Document Frequency
论文评审过程:Received 2 August 2007, Revised 4 April 2008, Accepted 13 April 2008, Available online 20 April 2008.
论文官网地址:https://doi.org/10.1016/j.knosys.2008.04.002