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Question Answering When Knowledge Bases are Incomplete

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Experimental IR Meets Multilinguality, Multimodality, and Interaction (CLEF 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12260))

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Abstract

While systems for question answering over knowledge bases (KB) continue to progress, real world usage requires systems that are robust to incomplete KBs. Dependence on the closed world assumption is highly problematic, as in many practical cases the information is constantly evolving and KBs cannot keep up. In this paper we formalize a typology of missing information in knowledge bases, and present a dataset based on the Spider KB question answering dataset, where we deliberately remove information from several knowledge bases, in this case implemented as relational databases (The dataset and the code to reproduce experiments are available at https://github.com/camillepradel/IDK.). Our dataset, called IDK (Incomplete Data in Knowledge base question answering), allows to perform studies on how to detect and recover from such cases. The analysis shows that simple baselines fail to detect most of the unanswerable questions.

This work has been supported by ERA-Net CHIST-ERA LIHLITH Project funded by the Agencia Estatal de Investigación (AEI, Spain) projects PCIN-2017-118/AEI and PCIN-2017-085/AEI, the Agence Nationale pour la Recherche (ANR, France) projects ANR-17-CHR2-0001-03 and ANR-17-CHR2-0001-04, and the Swiss National Science Foundation (SNF, Switzerland) project 20CH21 174237.

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Notes

  1. 1.

    https://www.w3.org/standards/semanticweb/.

  2. 2.

    None type corresponds to RDF plain literals: https://www.w3.org/TR/rdf-concepts/#dfn-plain-literal.

  3. 3.

    Plus domain and range properties, and labels language tags we did not include in our definition for the sake of simplicity.

  4. 4.

    We used Magnitude [12] in order to query the embeddings in a way that is robust to minor morphological word differences.

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Author information

Authors and Affiliations

  1. Synapse Développement, Toulouse, France

    Camille Pradel & Damien Sileo

  2. Universidad Nacional de Educación a Distanca, Madrid, Spain

    Álvaro Rodrigo & Anselmo Peñas

  3. Ixa NLP Group, University of the Basque Country UPV/EHU, Leioa, Spain

    Eneko Agirre

Authors
  1. Camille Pradel
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  2. Damien Sileo
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  3. Álvaro Rodrigo
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  4. Anselmo Peñas
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  5. Eneko Agirre
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Corresponding author

Correspondence to Camille Pradel .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, Greece

    Avi Arampatzis

  2. University of Amsterdam, Amsterdam, The Netherlands

    Evangelos Kanoulas

  3. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Theodora Tsikrika

  4. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Stefanos Vrochidis

  5. Faculty of Library, Information and Media Science, University of Tsukuba, Ibaraki, Japan

    Hideo Joho

  6. Department of Computer Science, University of Copenhagen, Copenhagen, Denmark

    Christina Lioma

  7. Brown University, Providence, RI, USA

    Carsten Eickhoff

  8. LIMSI-CNRS, Orsay, France

    Aurélie Névéol

  9. Department of Information Engineering, University of Padova, Padua, Italy

    Linda Cappellato

  10. Department of Information Engineering, University of Padova, Padua, Italy

    Nicola Ferro

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Pradel, C., Sileo, D., Rodrigo, Á., Peñas, A., Agirre, E. (2020). Question Answering When Knowledge Bases are Incomplete. In: Arampatzis, A., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2020. Lecture Notes in Computer Science(), vol 12260. Springer, Cham. https://doi.org/10.1007/978-3-030-58219-7_4

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  • DOI: https://doi.org/10.1007/978-3-030-58219-7_4

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