G-Bean: an ontology-graph based web tool for biomedical literature retrieval

BMC Bioinformatics

Research

Liang Dong¹  Lin Li²  Philip S Yu³  James Z Wang⁴  Yuanyuan Zhang⁴  Pradip K Srimani⁴ 

[1] Barnes and Noble, LLC, 10007, New York City, NY, USA;Department of Computer Science & Software Engineering, College of Science and Engineering, Seattle University, 98122, Seattle, WA, USA;Department of Computer Science, University of Illinois at Chicago, 60607, Chicago, IL, USA;School of Computing, Clemson University, 29634, Clemson, SC, USA;

关键词: Query Expansion;    MEDLINE Database;    Partial Index;    Index Creation;    Ontology Graph;   

DOI  :  10.1186/1471-2105-15-S12-S1

来源: Springer

【 摘 要 】

BackgroundCurrently, most people use NCBI's PubMed to search the MEDLINE database, an important bibliographical information source for life science and biomedical information. However, PubMed has some drawbacks that make it difficult to find relevant publications pertaining to users' individual intentions, especially for non-expert users. To ameliorate the disadvantages of PubMed, we developed G-Bean, a g raph based b iomedical sea rch en gine, to search biomedical articles in MEDLINE database more efficiently.MethodsG-Bean addresses PubMed's limitations with three innovations: (1) Parallel document index creation: a multithreaded index creation strategy is employed to generate the document index for G-Bean in parallel; (2) Ontology-graph based query expansion: an ontology graph is constructed by merging four major UMLS (Version 2013AA) vocabularies, MeSH, SNOMEDCT, CSP and AOD, to cover all concepts in National Library of Medicine (NLM) database; a Personalized PageRank algorithm is used to compute concept relevance in this ontology graph and the Term Frequency - Inverse Document Frequency (TF-IDF) weighting scheme is used to re-rank the concepts. The top 500 ranked concepts are selected for expanding the initial query to retrieve more accurate and relevant information; (3) Retrieval and re-ranking of documents based on user's search intention: after the user selects any article from the existing search results, G-Bean analyzes user's selections to determine his/her true search intention and then uses more relevant and more specific terms to retrieve additional related articles. The new articles are presented to the user in the order of their relevance to the already selected articles.ResultsPerformance evaluation with 106 OHSUMED benchmark queries shows that G-Bean returns more relevant results than PubMed does when using these queries to search the MEDLINE database. PubMed could not even return any search result for some OHSUMED queries because it failed to form the appropriate Boolean query statement automatically from the natural language query strings. G-Bean is available at http://bioinformatics.clemson.edu/G-Bean/index.php.ConclusionsG-Bean addresses PubMed's limitations with ontology-graph based query expansion, automatic document indexing, and user search intention discovery. It shows significant advantages in finding relevant articles from the MEDLINE database to meet the information need of the user.

【 授权许可】

CC BY   
© Wang et al.; licensee BioMed Central Ltd. 2014