Time in a Box: Advancing Knowledge Graph Completion with Temporal Scopes

Ling Cai, Krzysztof Janowicz, Bo Yan, Rui Zhu, Gengchen Mai

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

7 Citations (Scopus)
33 Downloads (Pure)

Abstract

Almost all statements in knowledge bases have a temporal scope during which they are valid. Hence, knowledge base completion (KBC) on temporal knowledge bases (TKB), where each statementmay be associated with a temporal scope, has attracted growing attention. Prior works assume that each statement in a TKBmust be associated with a temporal scope. This ignores the fact that the scoping information is commonly missing in a KB. Thus prior work is typically incapable of handling generic use cases where a TKB is composed of temporal statements with/without a known temporal scope. In order to address this issue, we establish a new knowledge base embedding framework, called TIME2BOX, that can deal with atemporal and temporal statements of different types simultaneously. Our main insight is that answers to a temporal query always belong to a subset of answers to a time-agnostic counterpart. Put differently, time is a filter that helps pick out answers to be correct during certain periods. We introduce boxes to represent a set of answer entities to a time-agnostic query. The filtering functionality of time is modeled by intersections over these boxes. In addition, we generalize current evaluation protocols on time interval prediction. We describe experiments on two datasets and show that the proposed method outperforms state-of-the-art (SOTA) methods on both link prediction and time prediction.

Original languageEnglish
Title of host publicationK-CAP 2021
Subtitle of host publicationProceedings of the 11th Knowledge Capture Conference
PublisherAssociation for Computing Machinery (ACM)
Pages121-128
Number of pages8
ISBN (Electronic)9781450384575
DOIs
Publication statusPublished - 2 Dec 2021
Event11th ACM International Conference on Knowledge Capture, K-CAP 2021 - Virtual, Online, United States
Duration: 2 Dec 20213 Dec 2021

Publication series

NameK-CAP 2021 - Proceedings of the 11th Knowledge Capture Conference

Conference

Conference11th ACM International Conference on Knowledge Capture, K-CAP 2021
Country/TerritoryUnited States
CityVirtual, Online
Period2/12/213/12/21

Bibliographical note

Funding Information:
This work was partially supported by the NSF award 2033521.

Publisher Copyright:
© 2021 ACM.

Keywords

  • link prediction
  • temporal knowledge base
  • time prediction
  • time2box

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