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Joint International Semantic Technology Conference

JIST 2018: Semantic Technology pp 77–85Cite as

On Enhancing Visual Query Building over KGs Using Query Logs

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11341))

Abstract

Knowledge Graphs have recently gained a lot of attention and have been successfully applied in both academia and industry. Since KGs may be very large: they may contain millions of entities and triples relating them to each other, to classes, and assigning them data values, it is important to provide endusers with effective tools to explore information encapsulated in KGs. In this work we present a visual query system that allows users to explore KGs by intuitively constructing tree-shaped conjunctive queries. It is known that systems of this kind suffer from the problem of information overflow: when constructing a query the users have to iteratively choose from a potentially very long list of options, sich as, entities, classes, and data values, where each such choice corresponds to an extension of the query new filters. In order to address this problem we propose an approach to substantially reduce such lists with the help of ranking and by eliminating the so-called deadends, options that yield queries with no answers over a given KG.

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Notes

  1. 1.

    Sesame is a widely-used Java framework for processing RDF data. It offers an easy-to-use API that can be connected to all leading RDF storage solutions.

  2. 2.

    Stardog is a Java-based triple store providing reasoning support for all OWL 2 profiles as well as a SPARQL implementation.

  3. 3.

    RDFox is an in-memory RDF triple store that supports shared memory parallel Datalog reasoning. It is written in C++ and comes with a Java wrapper allowing for a seamless integration with Java-based applications.

  4. 4.

    https://wiki.dbpedia.org/dbpedia-version-2016-04.

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Acknowledgements

This work is partially funded by EU H2020 TheyBuyForYou (780247) project, by the EPSRC projects MaSI\(^3\), DBOnto, ED\(^3\), and by the SIRIUS Centre, Norwegian Research Council project number 237898.

Author information

Authors and Affiliations

  1. University of Oslo, Oslo, Norway

    Vidar Klungre, Martin Giese, Arild Waaler & Evgeny Kharlamov

  2. Norwegian University of Science and Technology, Gøvik, Norway

    Ahmet Soylu

  3. SINTEF Digital, Oslo, Norway

    Ahmet Soylu

  4. University of Oxford, Oxford, UK

    Evgeny Kharlamov

Authors
  1. Vidar Klungre
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  2. Ahmet Soylu
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  3. Martin Giese
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  4. Arild Waaler
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  5. Evgeny Kharlamov
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Corresponding author

Correspondence to Vidar Klungre .

Editor information

Editors and Affiliations

  1. National Institute of Informatics, Tokyo, Japan

    Ryutaro Ichise

  2. Accenture Labs, Dublin, Ireland

    Freddy Lecue

  3. Japan Science and Technology Agency, Tokyo, Japan

    Takahiro Kawamura

  4. Peking University, Beijing, China

    Dongyan Zhao

  5. Imperial College London, London, UK

    Stephen Muggleton

  6. Osaka University, Osaka, Japan

    Kouji Kozaki

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Klungre, V., Soylu, A., Giese, M., Waaler, A., Kharlamov, E. (2018). On Enhancing Visual Query Building over KGs Using Query Logs. In: Ichise, R., Lecue, F., Kawamura, T., Zhao, D., Muggleton, S., Kozaki, K. (eds) Semantic Technology. JIST 2018. Lecture Notes in Computer Science(), vol 11341. Springer, Cham. https://doi.org/10.1007/978-3-030-04284-4_6

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  • DOI: https://doi.org/10.1007/978-3-030-04284-4_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04283-7

  • Online ISBN: 978-3-030-04284-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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