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HapPenIng: Happen, Predict, Infer—Event Series Completion in a Knowledge Graph

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Book cover The Semantic Web – ISWC 2019 (ISWC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11778))

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Abstract

Event series, such as the Wimbledon Championships and the US presidential elections, represent important happenings in key societal areas including sports, culture and politics. However, semantic reference sources, such as Wikidata, DBpedia and EventKG knowledge graphs, provide only an incomplete event series representation. In this paper we target the problem of event series completion in a knowledge graph. We address two tasks: (1) prediction of sub-event relations, and (2) inference of real-world events that happened as a part of event series and are missing in the knowledge graph. To address these problems, our proposed supervised HapPenIng approach leverages structural features of event series. HapPenIng does not require any external knowledge - the characteristics making it unique in the context of event inference. Our experimental evaluation demonstrates that HapPenIng outperforms the baselines by 44 and 52% points in terms of precision for the sub-event prediction and the inference tasks, correspondingly.

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Notes

  1. 1.

    http://eventkg.l3s.uni-hannover.de/happening.

  2. 2.

    https://www.wikidata.org/wiki/Q1656682.

  3. 3.

    https://www.wikidata.org/wiki/Property:P31.

  4. 4.

    https://www.wikidata.org/wiki/Property:P179.

  5. 5.

    For example, the event series “TED talk”, whose set of edition template labels (e.g. “Avi Reichental: What’s next in 3D printing” and “Amanda Palmer: The art of asking”) has a high Gini impurity, is not included in the set of event series.

  6. 6.

    http://eventkg.l3s.uni-hannover.de/happening.

  7. 7.

    Existing benchmark datasets do not contain a sufficient amount of sub-event relations. For example, FB15K [3] only contains 224 triples containing one of the Freebase predicates /time/event/includes_event, /time/event/included_in_event or /time/event/instance_of_recurring_event.

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Acknowledgements

This work was partially funded by the EU Horizon 2020 under MSCA-ITN-2018 “Cleopatra” (812997), and the Federal Ministry of Education and Research, Germany (BMBF) under “Simple-ML” (01IS18054).

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Authors and Affiliations

  1. L3S Research Center, Leibniz Universität Hannover, Hannover, Germany

    Simon Gottschalk & Elena Demidova

Authors
  1. Simon Gottschalk
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  2. Elena Demidova
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Corresponding author

Correspondence to Simon Gottschalk .

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Editors and Affiliations

  1. Fondazione Bruno Kessler, Trento, Italy

    Chiara Ghidini

  2. Linköping University, Linköping, Sweden

    Olaf Hartig

  3. University of Bonn, Bonn, Germany

    Maria Maleshkova

  4. University of Economics Prague, Prague, Czech Republic

    Vojtěch Svátek

  5. University of Illinois at Chicago, Chicago, IL, USA

    Isabel Cruz

  6. University of Chile, Santiago, Chile

    Aidan Hogan

  7. Memect Technology, Beijing, China

    Jie Song

  8. Mines Saint-Etienne, Saint-Etienne, France

    Maxime Lefrançois

  9. Inria Sophia Antipolis - Méditerranée, Sophia Antipolis, France

    Fabien Gandon

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Gottschalk, S., Demidova, E. (2019). HapPenIng: Happen, Predict, Infer—Event Series Completion in a Knowledge Graph. In: Ghidini, C., et al. The Semantic Web – ISWC 2019. ISWC 2019. Lecture Notes in Computer Science(), vol 11778. Springer, Cham. https://doi.org/10.1007/978-3-030-30793-6_12

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  • DOI: https://doi.org/10.1007/978-3-030-30793-6_12

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  • Online ISBN: 978-3-030-30793-6

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