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        <title>Deep Reinforcement Learning for Entity Alignment</title>
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        <namePart type="given">Lingbing</namePart>
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        <namePart type="given">Yuqiang</namePart>
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    <name type="personal">
        <namePart type="given">Huajun</namePart>
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    <abstract>Embedding-based methods have attracted increasing attention in recent entity alignment (EA) studies. Although great promise they can offer, there are still several limitations. The most notable is that they identify the aligned entities based on cosine similarity, ignoring the semantics underlying the embeddings themselves. Furthermore, these methods are shortsighted, heuristically selecting the closest entity as the target and allowing multiple entities to match the same candidate. To address these limitations, we model entity alignment as a sequential decision-making task, in which an agent sequentially decides whether two entities are matched or mismatched based on their representation vectors. The proposed reinforcement learning (RL)-based entity alignment framework can be flexibly adapted to most embedding-based EA methods. The experimental results demonstrate that it consistently advances the performance of several state-of-the-art methods, with a maximum improvement of 31.1% on Hits@1.</abstract>
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    <identifier type="doi">10.18653/v1/2022.findings-acl.217</identifier>
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%0 Conference Proceedings
%T Deep Reinforcement Learning for Entity Alignment
%A Guo, Lingbing
%A Han, Yuqiang
%A Zhang, Qiang
%A Chen, Huajun
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Findings of the Association for Computational Linguistics: ACL 2022
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F guo-etal-2022-deep
%X Embedding-based methods have attracted increasing attention in recent entity alignment (EA) studies. Although great promise they can offer, there are still several limitations. The most notable is that they identify the aligned entities based on cosine similarity, ignoring the semantics underlying the embeddings themselves. Furthermore, these methods are shortsighted, heuristically selecting the closest entity as the target and allowing multiple entities to match the same candidate. To address these limitations, we model entity alignment as a sequential decision-making task, in which an agent sequentially decides whether two entities are matched or mismatched based on their representation vectors. The proposed reinforcement learning (RL)-based entity alignment framework can be flexibly adapted to most embedding-based EA methods. The experimental results demonstrate that it consistently advances the performance of several state-of-the-art methods, with a maximum improvement of 31.1% on Hits@1.
%R 10.18653/v1/2022.findings-acl.217
%U https://aclanthology.org/2022.findings-acl.217/
%U https://doi.org/10.18653/v1/2022.findings-acl.217
%P 2754-2765

Markdown (Informal)

[Deep Reinforcement Learning for Entity Alignment](https://aclanthology.org/2022.findings-acl.217/) (Guo et al., Findings 2022)

• Deep Reinforcement Learning for Entity Alignment (Guo et al., Findings 2022)

ACL

• Lingbing Guo, Yuqiang Han, Qiang Zhang, and Huajun Chen. 2022. Deep Reinforcement Learning for Entity Alignment. In Findings of the Association for Computational Linguistics: ACL 2022, pages 2754–2765, Dublin, Ireland. Association for Computational Linguistics.