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Path-based reasoning with constrained type attention for knowledge graph completion

  • Kai Lei
  • Jin Zhang
  • Yuexiang Xie
  • Desi Wen
  • Daoyuan Chen
  • Min Yang
  • Ying ShenEmail author
Original Article
  • 56 Downloads

Abstract

Multi-hop reasoning over paths in knowledge graphs has attracted rising research interest in the field of knowledge graph completion. Entity types and relation types both contain various kinds of information content though only a subset of them are helpful in the specific triples. Although significant progress has been made by existing models, they have two major shortcomings. First, these models seldom learn an explicit representation of entities and relations with semantic information. Second, they reason without discriminating distinct role types that the same entity with multiple types plays in different triples. To address these issues, we develop a novel path-based reasoning with constrained type attention model, which tries to identify entity types by leveraging relation type constraints in the corresponding triples. Our experimental evaluation shows that the proposed model outperforms the state of the art on a real-world dataset. Further analyses also confirm that both word-level and triple-level attention mechanisms of our model are effective.

Keywords

Neural network Knowledge graph completion Multi-hop reasoning Attention mechanism 

Notes

Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (No. 61602013), Natural Science Foundation of Guangdong (No. 2018A030313017) and the Shenzhen Fundamental Research Project (No. JCYJ20170818091546869).

Compliance with ethical standards

Conflict of interest

The authors declare that there is no actual or potential conflict of interest in relation to this article.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Shenzhen Key Lab for Information Centric Networking and Blockchain Technology (ICNLAB), School of Electronics and Computer Engineering (SECE)Peking UniversityShenzhenPeople’s Republic of China
  2. 2.Shenzhen Institutes of Advanced Technology (SIAT)Chinese Academy of SciencesShenzhenPeople’s Republic of China
  3. 3.PCL Research Center of Networks and Communications, Peng Cheng LaboratoryShenzhenPeople’s Republic of China

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