TY - GEN
T1 - TEA: Time-aware Entity Alignment in Knowledge Graphs
AU - Liu, Yu
AU - Hua, Wen
AU - Xin, Kexuan
AU - Hosseini, Saeid
AU - Zhou, Xiaofang
N1 - Funding Information:
This work was supported by the Australian Research Council (grant no. DE210100160 and DP200103650), the Hong Kong Research Grants Council (grant no. 16202722), and Natural Science Foundation of China (grant no. 62072125). It was partially conducted in the JC STEM Lab of Data Science Foundations funded by The Hong Kong Jockey Club Charities Trust.
Publisher Copyright:
© 2023 ACM.
PY - 2023/4/30
Y1 - 2023/4/30
N2 - Entity alignment (EA) aims to identify equivalent entities between knowledge graphs (KGs), which is a key technique to improve the coverage of existing KGs. Current EA models largely ignore the importance of time information contained in KGs and treat relational facts or attribute values of entities as time-invariant. However, real-world entities could evolve over time, making the knowledge of the aligned entities very different in multiple KGs. This may cause incorrect matching between KGs if such entity dynamics is ignored. In this paper, we propose a time-aware entity alignment (TEA) model that discovers the entity evolving behaviour by exploring the time contexts in KGs and aggregates various contextual information to make the alignment decision. In particular, we address two main challenges in the TEA model: 1) How to identify highly-correlated temporal facts; 2) How to capture entity dynamics and incorporate it to learn a more informative entity representation for the alignment task. Experiments on real-world datasets1 verify the superiority of our TEA model over state-of-the-art entity aligners.
AB - Entity alignment (EA) aims to identify equivalent entities between knowledge graphs (KGs), which is a key technique to improve the coverage of existing KGs. Current EA models largely ignore the importance of time information contained in KGs and treat relational facts or attribute values of entities as time-invariant. However, real-world entities could evolve over time, making the knowledge of the aligned entities very different in multiple KGs. This may cause incorrect matching between KGs if such entity dynamics is ignored. In this paper, we propose a time-aware entity alignment (TEA) model that discovers the entity evolving behaviour by exploring the time contexts in KGs and aggregates various contextual information to make the alignment decision. In particular, we address two main challenges in the TEA model: 1) How to identify highly-correlated temporal facts; 2) How to capture entity dynamics and incorporate it to learn a more informative entity representation for the alignment task. Experiments on real-world datasets1 verify the superiority of our TEA model over state-of-the-art entity aligners.
KW - context evolving
KW - Entity alignment
KW - knowledge graph
KW - predicate clustering
KW - time context encoder
UR - http://www.scopus.com/inward/record.url?scp=85159369382&partnerID=8YFLogxK
U2 - 10.1145/3543507.3583317
DO - 10.1145/3543507.3583317
M3 - Conference article published in proceeding or book
AN - SCOPUS:85159369382
T3 - ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023
SP - 2591
EP - 2599
BT - ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023
PB - Association for Computing Machinery, Inc
T2 - 2023 World Wide Web Conference, WWW 2023
Y2 - 30 April 2023 through 4 May 2023
ER -