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Generating Compact and Relaxable Answers to Keyword Queries over Knowledge Graphs

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The Semantic Web – ISWC 2020 (ISWC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12506))

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Abstract

Keyword search has been a prominent approach to querying knowledge graphs. For exploratory search tasks, existing methods commonly extract subgraphs that are group Steiner trees (GSTs) as answers. However, a GST that connects all the query keywords may not exist, or may inevitably have a large and unfocused graph structure in contrast to users’ favor to a compact answer. Therefore, in this paper, we aim at generating compact but relaxable subgraphs as answers, i.e., we require a computed subgraph to have a bounded diameter but allow it to only connect an incomplete subset of query keywords. We formulate it as a new combinatorial optimization problem of computing a minimally relaxed answer with a compactness guarantee, and we present a novel best-first search algorithm. Extensive experiments showed that our approach efficiently computed compact answers of high completeness.

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Notes

  1. 1.

    https://github.com/nju-websoft/CORE.

  2. 2.

    http://www.dbis.informatik.uni-goettingen.de/Mondial/Mondial-RDF/mondial.rdf.

  3. 3.

    http://www.cs.toronto.edu/~oktie/linkedmdb/linkedmdb-latest-dump.zip.

  4. 4.

    http://downloads.dbpedia.org/2016-10/core-i18n/en/mappingbased_objects_en.tql.bz2.

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Acknowledgments

This work was supported in part by the National Key R&D Program of China (2018YFB1005100), in part by the NSFC (61772264), and in part by the Six Talent Peaks Program of Jiangsu Province (RJFW-011).

Author information

Authors and Affiliations

  1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Gong Cheng, Shuxin Li & Ke Zhang

  2. Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, USA

    Chengkai Li

Authors
  1. Gong Cheng
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  2. Shuxin Li
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  3. Ke Zhang
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  4. Chengkai Li
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Corresponding author

Correspondence to Gong Cheng .

Editor information

Editors and Affiliations

  1. University of Edinburgh, Edinburgh, UK

    Jeff Z. Pan

  2. University of Liverpool, Liverpool, UK

    Valentina Tamma

  3. University of Bari, Bari, Italy

    Claudia d’Amato

  4. University of California, Santa Barbara, Santa Barbara, CA, USA

    Krzysztof Janowicz

  5. California State University, Long Beach, Long Beach, CA, USA

    Bo Fu

  6. Vienna University of Economics and Business, Vienna, Austria

    Axel Polleres

  7. Rensselaer Polytechnic Institute, Troy, NY, USA

    Oshani Seneviratne

  8. Massachusetts Institute of Technology, Cambridge, MA, USA

    Lalana Kagal

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Cheng, G., Li, S., Zhang, K., Li, C. (2020). Generating Compact and Relaxable Answers to Keyword Queries over Knowledge Graphs. In: Pan, J.Z., et al. The Semantic Web – ISWC 2020. ISWC 2020. Lecture Notes in Computer Science(), vol 12506. Springer, Cham. https://doi.org/10.1007/978-3-030-62419-4_7

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  • DOI: https://doi.org/10.1007/978-3-030-62419-4_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62418-7

  • Online ISBN: 978-3-030-62419-4

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