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Towards Temporal Knowledge Graph Embeddings with Arbitrary Time Precision

Leblay, Julien, Chekol, Melisachew Wudage, Liu, Xin·2020·Proceedings of the 29th ACM International Conference on Information & Knowledge Management
DOI 10.1145/3340531.3412028
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Abstract

Acknowledging the dynamic nature of knowledge graphs, the problem of learning temporal knowledge graph embeddings has recently gained attention. Essentially, the goal is to learn vector representation for the nodes and edges of a knowledge graph taking time into account. These representations must preserve certain properties of the original graph, so as to allow not only classification or clustering tasks, as for classical graph embeddings, but also approximate time-dependent query answering or link predictions over knowledge graphs. For instance, "who was the leader of Germany in 1994?'' or "when was Bonn the capital of Germany?''

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  • [Leblay et al., 2020] encodes a timestamp into a one-hot vector; different regions represent different time resolutions; the one-hot vector corresponds to linear transformation matrices used to map entities/relations to be time-dependent.

    “To achieve arbitrary time precision, [Leblay et al., 2020] first encodes a timestamp into a one-hot vector; and different regions in the vector represent different time resolutions, e.g., centuries or days.The one-hot vector of the timestamp corresponds to a series of linear transformation matrices, which are used to map entities/relations to be time-dependent.”

    From Temporal Knowledge Graph Completion: A Survey (2023) · cites this paper as Leblay et al., 2020

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Metadata

Authors
Leblay, J., Chekol, M. W., & Liu, X.
Affiliations
  • Rakuten, Inc., Tokyo, Japan
  • Utrecht University, Utrecht, Netherlands
  • AIRC/RWBC-OIL, AIST, Tokyo, Japan
Venue
Proceedings of the 29th ACM International Conference on Information & Knowledge Management
Publisher
ACM
Published
2020-10-19
Pages
685-694
DOI
10.1145/3340531.3412028
Cited by
15
References
2
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Funders
  • NEDO
  • JSPS Grant-in-Aid for Early-Career Scientists (19K20352)

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