TY - GEN
T1 - An Effective and Efficient Entity Alignment Decoding Algorithm via Third-Order Tensor Isomorphism
AU - Mao, Xin
AU - Ma, Meirong
AU - Yuan, Hao
AU - Zhu, Jianchao
AU - Wang, Zongyu
AU - Xie, Rui
AU - Wu, Wei
AU - Lan, Man
N1 - Publisher Copyright:
© 2022 Association for Computational Linguistics.
PY - 2022
Y1 - 2022
N2 - Entity alignment (EA) aims to find the equivalent entity pairs between KGs, which is a crucial step for integrating multi-source KGs. For a long time, most researchers have regarded EA as a pure graph representation learning task and focused on improving graph encoders while paying little attention to the decoding process. In this paper, we propose an effective and efficient EA Decoding Algorithm via Third-order Tensor Isomorphism (DATTI). Specifically, we derive two sets of isomorphism equations: (1) Adjacency tensor isomorphism equations and (2) Gramian tensor isomorphism equations. By combining these equations, DATTI could effectively utilize the adjacency and inner correlation isomorphisms of KGs to enhance the decoding process of EA. Extensive experiments on public datasets indicate that our decoding algorithm can deliver significant performance improvements even on the most advanced EA methods, while the extra required time is less than 3 seconds.
AB - Entity alignment (EA) aims to find the equivalent entity pairs between KGs, which is a crucial step for integrating multi-source KGs. For a long time, most researchers have regarded EA as a pure graph representation learning task and focused on improving graph encoders while paying little attention to the decoding process. In this paper, we propose an effective and efficient EA Decoding Algorithm via Third-order Tensor Isomorphism (DATTI). Specifically, we derive two sets of isomorphism equations: (1) Adjacency tensor isomorphism equations and (2) Gramian tensor isomorphism equations. By combining these equations, DATTI could effectively utilize the adjacency and inner correlation isomorphisms of KGs to enhance the decoding process of EA. Extensive experiments on public datasets indicate that our decoding algorithm can deliver significant performance improvements even on the most advanced EA methods, while the extra required time is less than 3 seconds.
UR - https://www.scopus.com/pages/publications/85138853765
U2 - 10.18653/v1/2022.acl-long.405
DO - 10.18653/v1/2022.acl-long.405
M3 - 会议稿件
AN - SCOPUS:85138853765
T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics
SP - 5888
EP - 5898
BT - ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)
A2 - Muresan, Smaranda
A2 - Nakov, Preslav
A2 - Villavicencio, Aline
PB - Association for Computational Linguistics (ACL)
T2 - 60th Annual Meeting of the Association for Computational Linguistics, ACL 2022
Y2 - 22 May 2022 through 27 May 2022
ER -