A Simple Temporal Information Matching Mechanism for Entity Alignment between Temporal Knowledge Graphs

Li Cai, Xin Mao, Meirong Ma, Hao Yuan, Jianchao Zhu, Man Lan


Abstract
Entity alignment (EA) aims to find entities in different knowledge graphs (KGs) that refer to the same object in the real world. Recent studies incorporate temporal information to augment the representations of KGs. The existing methods for EA between temporal KGs (TKGs) utilize a time-aware attention mechanisms to incorporate relational and temporal information into entity embeddings. The approaches outperform the previous methods by using temporal information. However, we believe that it is not necessary to learn the embeddings of temporal information in KGs since most TKGs have uniform temporal representations. Therefore, we propose a simple GNN model combined with a temporal information matching mechanism, which achieves better performance with less time and fewer parameters. Furthermore, since alignment seeds are difficult to label in real-world applications, we also propose a method to generate unsupervised alignment seeds via the temporal information of TKG. Extensive experiments on public datasets indicate that our supervised method significantly outperforms the previous methods and the unsupervised one has competitive performance.
Anthology ID:
2022.coling-1.181
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2075–2086
Language:
URL:
https://aclanthology.org/2022.coling-1.181
DOI:
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Cite (ACL):
Li Cai, Xin Mao, Meirong Ma, Hao Yuan, Jianchao Zhu, and Man Lan. 2022. A Simple Temporal Information Matching Mechanism for Entity Alignment between Temporal Knowledge Graphs. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2075–2086, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
A Simple Temporal Information Matching Mechanism for Entity Alignment between Temporal Knowledge Graphs (Cai et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.181.pdf