DA-Net: Distributed attention network for temporal knowledge graph reasoning

Kangzheng LIU, Feng ZHAO, Hongxu CHEN, Yicong LI, Guandong XU, Hai JIN

Research output: Chapter in Book/Report/Conference proceedingChapters

6 Citations (Scopus)

Abstract

Predicting future events in dynamic knowledge graphs has attracted significant attention. Existing work models the historical information in a holistic way, which achieves satisfactory performance. However, in real-world scenarios, the influence of historical information on future events is changing over time. Therefore, it is difficult to distinguish the historical information of different roles by invariably embedding historical entities with simple vector stacking. Furthermore, it is laborious to explicitly learn a distributed representation of each historical repetitive fact at different timestamps. This poses a challenge to the widely adopted codec-based architectures. In this paper, we propose a novel model for predicting future events, namely Distributed Attention Network (DA-Net). Rather than obtaining the fixed representations of historical events, DA-Net attempts to learn the distributed attention of future events on repetitive facts at different historical timestamps inspired by human cognitive theory. In human cognitive theory, when humans make a decision, similar historical events are replayed during memory recall. Based on memory, the original intention is adjusted according to their recent knowledge developments, making the action more reasonable to the context. Experiments on four benchmark datasets demonstrate a substantial improvement of DA-Net on multiple evaluation metrics. Copyright © 2022 Association for Computing Machinery.

Original languageEnglish
Title of host publicationProceedings of the 31st ACM International Conference on Information & Knowledge Management
Place of PublicationNew York
PublisherAssociation for Computing Machinery
Pages1289-1298
ISBN (Electronic)9781450392365
DOIs
Publication statusPublished - Oct 2022

Citation

Liu, K., Zhao, F., Chen, H., Li, Y., Xu, G., & Jin, H. (2022). DA-Net: Distributed attention network for temporal knowledge graph reasoning. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (pp. 1289-1298). Association for Computing Machinery. https://doi.org/10.1145/3511808.3557280

Keywords

  • Knowledge graphs
  • Temporal reasoning
  • Cognitive modeling

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