Message passing for hyper-relational knowledge graphs

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Authors

  • Mikhail Galkin
  • Priyansh Trivedi
  • Gaurav Maheshwari
  • Ricardo Usbeck
  • Jens Lehmann

Hyper-relational knowledge graphs (KGs) (e.g., Wikidata) enable associating additional key-value pairs along with the main triple to disambiguate, or restrict the validity of a fact. In this work, we propose a message passing based graph encoder - STARE capable of modeling such hyper-relational KGs. Unlike existing approaches, STARE can encode an arbitrary number of additional information (qualifiers) along with the main triple while keeping the semantic roles of qualifiers and triples intact. We also demonstrate that existing benchmarks for evaluating link prediction (LP) performance on hyper-relational KGs suffer from fundamental flaws and thus develop a new Wikidata-based dataset - WD50K. Our experiments demonstrate that STARE based LP model outperforms existing approaches across multiple benchmarks. We also confirm that leveraging qualifiers is vital for link prediction with gains up to 25 MRR points compared to triple-based representations.

OriginalspracheEnglisch
TitelEMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
HerausgeberBonnie Webber, Trevor Cohn, Yulan He, Yang Liu
Anzahl der Seiten14
VerlagAssociation for Computational Linguistics (ACL)
Erscheinungsdatum01.01.2020
Seiten7346-7359
ISBN (elektronisch)9781952148606
DOIs
PublikationsstatusErschienen - 01.01.2020
Extern publiziertJa
Veranstaltung2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020 - Virtual, Online
Dauer: 16.11.202020.11.2020
https://2020.emnlp.org

DOI

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