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International Conference on Flexible Query Answering Systems

FQAS 2019: Flexible Query Answering Systems pp 215–227Cite as

Semantic Understanding of Natural Language Stories for Near Human Question Answering

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11529))

Abstract

Machine understanding of natural language stories is complex, and automated question answering based on them requires careful knowledge engineering involving knowledge representation, deduction, context recognition and sentiment analysis. In this paper, we present an approach to near human question answering based on natural language stories. We show that translating stories into knowledge graphs in RDF, and then restating the natural language questions into SPARQL to answer queries can be successful if the RDF graph is augmented with an ontology and an inference engine. By leveraging existing knowledge processing engines such as FRED and NLQuery, we propose the contours of an open-ended and online flexible query answering system, called Omniscient, that is able to accept a natural language user story and respond to questions also framed in natural language. The novelty of Omniscient is in its ability to recognize context and respond deductively that most current knowledge processing systems are unable to do.

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Notes

  1. 1.

    https://qald.sebastianwalter.org/index.php?x=home&q=home.

  2. 2.

    https://blog.ayoungprogrammer.com/2016/10/natural-lang-query-engine.html/.

  3. 3.

    http://nlp.stanford.edu:8080/corenlp/.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Idaho, Moscow, ID, USA

    Hasan M. Jamil & Joel Oduro-Afriyie

Authors
  1. Hasan M. Jamil
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  2. Joel Oduro-Afriyie
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Corresponding author

Correspondence to Hasan M. Jamil .

Editor information

Editors and Affiliations

  1. University of Calabria, Rende, Italy

    Alfredo Cuzzocrea

  2. University of Calabria, Rende, Italy

    Sergio Greco

  3. Legind Technologies , Esbjerg , Denmark

    Henrik Legind Larsen

  4. University of Calabria, Rende, Italy

    Domenico Saccà

  5. Roskilde University, Roskilde, Denmark

    Troels Andreasen

  6. Roskilde University, Roskilde, Denmark

    Henning Christiansen

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Jamil, H.M., Oduro-Afriyie, J. (2019). Semantic Understanding of Natural Language Stories for Near Human Question Answering. In: Cuzzocrea, A., Greco, S., Larsen, H., Saccà, D., Andreasen, T., Christiansen, H. (eds) Flexible Query Answering Systems. FQAS 2019. Lecture Notes in Computer Science(), vol 11529. Springer, Cham. https://doi.org/10.1007/978-3-030-27629-4_21

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  • DOI: https://doi.org/10.1007/978-3-030-27629-4_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-27628-7

  • Online ISBN: 978-3-030-27629-4

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