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Fast ObjectRank for Large Knowledge Databases

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The Semantic Web – ISWC 2021 (ISWC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12922))

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Abstract

ObjectRank is an essential tool to evaluate an importance of nodes for a user-specified query in heterogeneous graphs. However, existing methods are not applicable to massive graphs because they iteratively compute all nodes and edges. This paper proposes SchemaRank, which detects the exact top-k important nodes for a given query within a short running time. SchemaRank dynamically excludes unpromising nodes and edges, ensuring that it detects the same top-k important nodes as ObjectRank. Our extensive evaluations demonstrate that the running time of SchemaRank outperforms existing methods by up to two orders of magnitude.

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Notes

  1. 1.

    ObjectRank can easily handle updates of graphs (e.g., nodes/edges insertion or deletion, and changes of weights) by using the Gauss-Southwell algorithm. Thus, this work does not consider such updates.

  2. 2.

    All datasets are publickly available at https://aminer.org/citation.

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Acknowledgement

This work was partially supported by JSPS KAKENHI Early-Career Scientists (Grant Number JP18K18057) and JST PRESTO JPMJPR2033, Japan. I thank to Hiroyuki Kitagawa, Toshiyuki Amagasa, and Tomoki Sato for their helps and insightful discussions.

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Authors and Affiliations

  1. Center for Computational Sciences, University of Tsukuba, Tsukuba, Japan

    Hiroaki Shiokawa

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  1. Hiroaki Shiokawa
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Correspondence to Hiroaki Shiokawa .

Editor information

Editors and Affiliations

  1. University of Würzburg, Würzburg, Germany

    Andreas Hotho

  2. Linköping University, Linköping, Sweden

    Eva Blomqvist

  3. University of Düsseldorf, Düsseldorf, Germany

    Stefan Dietze

  4. IBM Research - Thomas J. Watson Research, Hawthorne, CA, USA

    Achille Fokoue

  5. University of Texas, Austin, TX, USA

    Ying Ding

  6. Imperial College, London, UK

    Payam Barnaghi

  7. Australian National University, Canberra, ACT, Australia

    Armin Haller

  8. Fondazione Bruno Kessler, Povo, Trento, Italy

    Mauro Dragoni

  9. The Open University Walton Hall, Milton Keynes, UK

    Harith Alani

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Shiokawa, H. (2021). Fast ObjectRank for Large Knowledge Databases. In: Hotho, A., et al. The Semantic Web – ISWC 2021. ISWC 2021. Lecture Notes in Computer Science(), vol 12922. Springer, Cham. https://doi.org/10.1007/978-3-030-88361-4_13

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  • DOI: https://doi.org/10.1007/978-3-030-88361-4_13

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  • Online ISBN: 978-3-030-88361-4

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