NIF LinkOut Portal

Options
Only Pubmed Central
Include Pubmed Central
Sections
Title
Abstract
Introduction
Methods
Results
Supplement
Appendix
Contributions
Background
Commentary
Funding
Limitations
Caption
FILTERS

Enabling multi-level relevance feedback on PubMed by integrating rank learning into DBMS.

Authors:
Yu H, Kim T, Oh J, Ko I, Kim S, Han WS
Affiliation:
Journal:
BMC bioinformatics

Abstract

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.

  1. Welcome

    Welcome to NIF. Explore available research resources: data, tools and materials, from across the web

  2. Community Resources

    Search for resources specially selected for NIF community

  3. More Resources

    Search across hundreds of additional biomedical databases

  4. Literature

    Search Pub Med abstracts and full text from PubMed Central

  5. Insert your Query

    Enter your search terms here and hit return. Search results for the selected tab will be returned.

  6. Join the Community

    Click here to login or register and join this community.

  7. Categories

    Narrow your search by selecting a category. For additional help in searching, view our tutorials.

  8. Query Info

    Displays the total number of search results. Provides additional information on search terms, e.g., automated query expansions, and any included categories or facets. Expansions, filters and facets can be removed by clicking on the X. Clicking on the + restores them.

  9. Search Results

    Displays individual records and a brief description. Click on the icons below each record to explore additional display options.

X