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Supporting Relevance Feedback with Concept Learning for Semantic Information Retrieval in Large OWL Knowledge Base

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Knowledge Management and Acquisition for Intelligent Systems (PKAW 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11016))

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Abstract

Relevance feedback in information retrieval is a popular way to learn the user’s intents. We investigate the feasibility and methodology of applying the concept learning in OWL knowledge base to deal with feedback in interactive information retrieval system. The feedback from the initial search results is considered as examples, and then the inductive concept learning technique is employed to generate a concept that describes the user’s requirement. We deal with the performance of concept learning by reducing the scale of the problem; a clustering based OWL knowledge base partitioning method is proposed to divide the knowledge base into several small-scale ones with acceptable recall and precision. An interactive healthcare information retrieval prototype is developed for evaluation; the results of the user study and precision-recall graph show the efficiency of the methods proposed.

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Acknowledgement

Supported by the Fundamental Research Funds for the Central Universities (No. GK201503066)

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Authors and Affiliations

  1. College of Computer Science, Shaanxi Normal University, Xi’an, China

    Liu Yuan

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  1. Liu Yuan
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Correspondence to Liu Yuan .

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Editors and Affiliations

  1. University of Tsukuba , Tokyo, Japan

    Kenichi Yoshida

  2. Shih Chien University, Taipei City, Taiwan

    Maria Lee

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Yuan, L. (2018). Supporting Relevance Feedback with Concept Learning for Semantic Information Retrieval in Large OWL Knowledge Base. In: Yoshida, K., Lee, M. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2018. Lecture Notes in Computer Science(), vol 11016. Springer, Cham. https://doi.org/10.1007/978-3-319-97289-3_5

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  • DOI: https://doi.org/10.1007/978-3-319-97289-3_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97288-6

  • Online ISBN: 978-3-319-97289-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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