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1. Yu H, Kim T, Oh J, Ko I, Kim S, Han WS: Enabling multi-level relevance feedback on PubMed by integrating rank learning into DBMS. BMC Bioinformatics; 2010;11 Suppl 2:S6
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Enabling multi-level relevance feedback on PubMed by integrating rank learning into DBMS.
  • BACKGROUND: Finding relevant articles from PubMed is challenging because it is hard to express the user's specific intention in the given query interface, and a keyword query typically retrieves a large number of results.
  • Researchers have applied machine learning techniques to find relevant articles by ranking the articles according to the learned relevance function.
  • However, the process of learning and ranking is usually done offline without integrated with the keyword queries, and the users have to provide a large amount of training documents to get a reasonable learning accuracy.
  • This paper proposes a novel multi-level relevance feedback system for PubMed, called RefMed, which supports both ad-hoc keyword queries and a multi-level relevance feedback in real time on PubMed.
  • RESULTS: RefMed supports a multi-level relevance feedback by using the RankSVM as the learning method, and thus it achieves higher accuracy with less feedback.
  • RefMed "tightly" integrates the RankSVM into RDBMS to support both keyword queries and the multi-level relevance feedback in real time; the tight coupling of the RankSVM and DBMS substantially improves the processing time.
  • An efficient parameter selection method for the RankSVM is also proposed, which tunes the RankSVM parameter without performing validation.
  • Thereby, RefMed achieves a high learning accuracy in real time without performing a validation process.
  • RefMed is accessible at http://dm.postech.ac.kr/refmed.
  • CONCLUSIONS: RefMed is the first multi-level relevance feedback system for PubMed, which achieves a high accuracy with less feedback.
  • It effectively learns an accurate relevance function from the user's feedback and efficiently processes the function to return relevant articles in real time.
  • [MeSH-major] Algorithms. Artificial Intelligence. Computational Biology / methods. Database Management Systems. PubMed
  • [MeSH-minor] Data Interpretation, Statistical. Feedback. Reproducibility of Results. User-Computer Interface

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  • (PMID = 20406504.001).
  • [ISSN] 1471-2105
  • [Journal-full-title] BMC bioinformatics
  • [ISO-abbreviation] BMC Bioinformatics
  • [Language] eng
  • [Publication-type] Journal Article; Research Support, Non-U.S. Gov't
  • [Publication-country] England
  • [Other-IDs] NLM/ PMC3165966
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