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Knowledge-Based Approach to Question Answering System Selection

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Computational Collective Intelligence

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9329))

Abstract

A growth of data published on the Web is still observed. Keyword-based search, used by most search engines, is a common way of information retrieval on the Web. Subsequently, keyword-based search may provide a huge amount of retrieved valueless information. This problem can be solved by Question Answering System (QAS, QA system). One of the challenging tasks for available QA systems is to understand the natural language questions correctly and deduce the precise meaning to retrieve accurate responses. A significant role of QA and an increasing number of them may cause a problem with selection the most suitable QA system. The general aim of this paper is to provide knowledge-based approach to QA system selection. It should ensure knowledge systematization and help users to find a proper solution that meets their needs.

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Author information

Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, West-Pomeranian University of Technology, Szczecin, Poland

    Agnieszka Konys

Authors
  1. Agnieszka Konys
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Correspondence to Agnieszka Konys .

Editor information

Editors and Affiliations

  1. Universidad Complutense de Madrid, Madrid, Spain

    Manuel Núñez

  2. Wrocław University of Technology, Wroclaw, Poland

    Ngoc Thanh Nguyen

  3. Computer Science Department, Universidad Autónoma De Madrid, Madrid, Spain

    David Camacho

  4. Wrocław University of Technology, Wroclaw, Poland

    Bogdan Trawiński

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Konys, A. (2015). Knowledge-Based Approach to Question Answering System Selection. In: Núñez, M., Nguyen, N., Camacho, D., Trawiński, B. (eds) Computational Collective Intelligence. Lecture Notes in Computer Science(), vol 9329. Springer, Cham. https://doi.org/10.1007/978-3-319-24069-5_34

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  • DOI: https://doi.org/10.1007/978-3-319-24069-5_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24068-8

  • Online ISBN: 978-3-319-24069-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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