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Semantic Graph based Pseudo Relevance Feedback for Biomedical Information Retrieval

Published: 19 December 2016 Publication History

Abstract

This paper proposed a novel pseudo relevance feedback strategy to facilitate the retrieval of more relevant biomedical documents by improving the quality of both feedback documents and expansion terms. Firstly, an ontology-graph based query expansion technique is applied to retrieve more relevant feedback documents. Secondly, useful expansion terms are extracted from the feedback documents based on a semantic graph based ranking approach. We add the expansion terms to the user query to retrieve more relevant documents. We use 10-fold cross validation technique to evaluate the performance of the proposed pseudo relevance feedback strategy over OHSUMED test collection. The experimental results demonstrate that the proposed strategy improves the retrieval performance by 33.8% over free-text based query in 11-point average precision. The proposed strategy also achieves better retrieval performance than two representative pseudo relevance feedback approaches. We have integrated this new strategy into G-Bean, a graph-based biomedical search engine. G-Bean is available at: http://bioinformatics.clemson.edu:8080/G-Bean/index.jsp

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  • (2017)A knowledge-driven approach for personalized literature recommendation based on deep semantic discriminationProceedings of the International Conference on Web Intelligence10.1145/3106426.3109439(1253-1259)Online publication date: 23-Aug-2017
  1. Semantic Graph based Pseudo Relevance Feedback for Biomedical Information Retrieval

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    cover image ACM Other conferences
    CSBio '16: Proceedings of the 7th International Conference on Computational Systems-Biology and Bioinformatics
    December 2016
    68 pages
    ISBN:9781450347945
    DOI:10.1145/3029375
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 19 December 2016

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    Author Tags

    1. biomedical information retrieval
    2. pseudo relevance feedback
    3. search engine
    4. semantic graph

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    • (2017)A knowledge-driven approach for personalized literature recommendation based on deep semantic discriminationProceedings of the International Conference on Web Intelligence10.1145/3106426.3109439(1253-1259)Online publication date: 23-Aug-2017

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