Unsupervised Entity Alignment for Temporal Knowledge Graphs

Xiaoze Liu, Junyang Wu, Tianyi Li, Lu Chen*, Yunjun Gao

*Corresponding author for this work

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

11 Citations (Scopus)

Abstract

Entity alignment (EA) is a fundamental data integration task that identifies equivalent entities between different knowledge graphs (KGs).
Temporal Knowledge graphs (TKGs) extend traditional knowledge
graphs by introducing timestamps, which have received increasing
attention. State-of-the-art time-aware EA studies have suggested that
the temporal information of TKGs facilitates the performance of EA.
However, existing studies have not thoroughly exploited the advantages of temporal information in TKGs. Also, they perform EA by
pre-aligning entity pairs, which can be labor-intensive and thus inefficient. In this paper, we present DualMatch that effectively fuses
the relational and temporal information for EA. DualMatch transfers
EA on TKGs into a weighted graph matching problem. More specifically, DualMatch is equipped with an unsupervised method, which
achieves EA without necessitating the seed alignment. DualMatch
has two steps: (i) encoding temporal and relational information into
embeddings separately using a novel label-free encoder, Dual-Encoder;
and (ii) fusing both information and transforming it into alignment using a novel graph-matching-based decoder, GM-Decoder. DualMatch
is able to perform EA on TKGs with or without supervision, due to
its capability of effectively capturing temporal information. Extensive
experiments on three real-world TKG datasets offer the insight that
DualMatch significantly outperforms the state-of-the-art methods.
Original languageEnglish
Title of host publicationACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023
Number of pages11
PublisherAssociation for Computing Machinery
Publication date30 Apr 2023
Pages2528–2538
ISBN (Electronic)978-1-4503-9416-1
DOIs
Publication statusPublished - 30 Apr 2023
Event2023 World Wide Web Conference, WWW 2023 - Austin, United States
Duration: 30 Apr 20234 May 2023

Conference

Conference2023 World Wide Web Conference, WWW 2023
Country/TerritoryUnited States
CityAustin
Period30/04/202304/05/2023
SponsorACM SIGWEB, Amazon Science, Baidu, et al., Megagon Labs, Zhipu AI

Bibliographical note

Funding Information:
This work was supported in part by the National Key Research and Development Program of China under Grant No. 2021YFC3300303, the NSFC under Grants No. (62025206, 61972338, and 62102351), and the Ningbo Science and Technology Special Innovation Projects with Grant Nos. 2022Z095 and 2021Z019. Lu Chen is the corresponding author of the work.

Publisher Copyright:
© 2023 ACM.

Keywords

  • Entity Alignment
  • Knowledge Graphs
  • Unsupervised Learning

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