@inproceedings{wang-etal-2025-rethinking,
title = "Rethinking Smoothness for Fast and Adaptable Entity Alignment Decoding",
author = "Wang, Yuanyi and
Li, Han and
Sun, Haifeng and
Zhang, Lei and
He, Bo and
Tang, Wei and
Yan, Tianhao and
Qi, Qi and
Wang, Jingyu",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.255/",
doi = "10.18653/v1/2025.findings-naacl.255",
pages = "4521--4535",
ISBN = "979-8-89176-195-7",
abstract = "Entity alignment (EA) is crucial for integrating multi-source knowledge graphs (KGs), aiming to identify equivalent entities across different graphs. However, most existing EA decoding methods rely on both entity and relation embeddings, limiting their generalizability and efficiency, especially in GNN-based models. To address these challenges, we propose Triple Feature Propagation (TFP), an adaptable and fast EA decoding framework that only utilizes entity embeddings. TFP reconstructs KG representation by maximizing the smoothness of entity embeddings. The discretized smoothness-maximization process yields the explicit Euler solution of TFP. We also generalize multi-view matrices: entity-to-entity, entity-to-relation, relation-to-entity, and relation-to-triple, to capture structural diversity. Extensive experiments on public datasets demonstrate that TFP is fast and adaptable to various encoders, achieving comparable results to state-of-the-art methods in under 6 seconds, and surpassing them in many cases."
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<abstract>Entity alignment (EA) is crucial for integrating multi-source knowledge graphs (KGs), aiming to identify equivalent entities across different graphs. However, most existing EA decoding methods rely on both entity and relation embeddings, limiting their generalizability and efficiency, especially in GNN-based models. To address these challenges, we propose Triple Feature Propagation (TFP), an adaptable and fast EA decoding framework that only utilizes entity embeddings. TFP reconstructs KG representation by maximizing the smoothness of entity embeddings. The discretized smoothness-maximization process yields the explicit Euler solution of TFP. We also generalize multi-view matrices: entity-to-entity, entity-to-relation, relation-to-entity, and relation-to-triple, to capture structural diversity. Extensive experiments on public datasets demonstrate that TFP is fast and adaptable to various encoders, achieving comparable results to state-of-the-art methods in under 6 seconds, and surpassing them in many cases.</abstract>
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%0 Conference Proceedings
%T Rethinking Smoothness for Fast and Adaptable Entity Alignment Decoding
%A Wang, Yuanyi
%A Li, Han
%A Sun, Haifeng
%A Zhang, Lei
%A He, Bo
%A Tang, Wei
%A Yan, Tianhao
%A Qi, Qi
%A Wang, Jingyu
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F wang-etal-2025-rethinking
%X Entity alignment (EA) is crucial for integrating multi-source knowledge graphs (KGs), aiming to identify equivalent entities across different graphs. However, most existing EA decoding methods rely on both entity and relation embeddings, limiting their generalizability and efficiency, especially in GNN-based models. To address these challenges, we propose Triple Feature Propagation (TFP), an adaptable and fast EA decoding framework that only utilizes entity embeddings. TFP reconstructs KG representation by maximizing the smoothness of entity embeddings. The discretized smoothness-maximization process yields the explicit Euler solution of TFP. We also generalize multi-view matrices: entity-to-entity, entity-to-relation, relation-to-entity, and relation-to-triple, to capture structural diversity. Extensive experiments on public datasets demonstrate that TFP is fast and adaptable to various encoders, achieving comparable results to state-of-the-art methods in under 6 seconds, and surpassing them in many cases.
%R 10.18653/v1/2025.findings-naacl.255
%U https://aclanthology.org/2025.findings-naacl.255/
%U https://doi.org/10.18653/v1/2025.findings-naacl.255
%P 4521-4535
Markdown (Informal)
[Rethinking Smoothness for Fast and Adaptable Entity Alignment Decoding](https://aclanthology.org/2025.findings-naacl.255/) (Wang et al., Findings 2025)
ACL
- Yuanyi Wang, Han Li, Haifeng Sun, Lei Zhang, Bo He, Wei Tang, Tianhao Yan, Qi Qi, and Jingyu Wang. 2025. Rethinking Smoothness for Fast and Adaptable Entity Alignment Decoding. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 4521–4535, Albuquerque, New Mexico. Association for Computational Linguistics.