@inproceedings{liu-etal-2022-guiding,
title = "Guiding Neural Entity Alignment with Compatibility",
author = "Liu, Bing and
Scells, Harrisen and
Hua, Wen and
Zuccon, Guido and
Zhao, Genghong and
Zhang, Xia",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.32/",
doi = "10.18653/v1/2022.emnlp-main.32",
pages = "491--504",
abstract = "Entity Alignment (EA) aims to find equivalent entities between two Knowledge Graphs (KGs). While numerous neural EA models have been devised, they are mainly learned using labelled data only. In this work, we argue that different entities within one KG should have compatible counterparts in the other KG due to the potential dependencies among the entities. Making compatible predictions thus should be one of the goals of training an EA model along with fitting the labelled data: this aspect however is neglected in current methods. To power neural EA models with compatibility, we devise a training framework by addressing three problems: (1) how to measure the compatibility of an EA model; (2) how to inject the property of being compatible into an EA model; (3) how to optimise parameters of the compatibility model. Extensive experiments on widely-used datasets demonstrate the advantages of integrating compatibility within EA models. In fact, state-of-the-art neural EA models trained within our framework using just 5{\%} of the labelled data can achieve comparable effectiveness with supervised training using 20{\%} of the labelled data."
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<abstract>Entity Alignment (EA) aims to find equivalent entities between two Knowledge Graphs (KGs). While numerous neural EA models have been devised, they are mainly learned using labelled data only. In this work, we argue that different entities within one KG should have compatible counterparts in the other KG due to the potential dependencies among the entities. Making compatible predictions thus should be one of the goals of training an EA model along with fitting the labelled data: this aspect however is neglected in current methods. To power neural EA models with compatibility, we devise a training framework by addressing three problems: (1) how to measure the compatibility of an EA model; (2) how to inject the property of being compatible into an EA model; (3) how to optimise parameters of the compatibility model. Extensive experiments on widely-used datasets demonstrate the advantages of integrating compatibility within EA models. In fact, state-of-the-art neural EA models trained within our framework using just 5% of the labelled data can achieve comparable effectiveness with supervised training using 20% of the labelled data.</abstract>
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%0 Conference Proceedings
%T Guiding Neural Entity Alignment with Compatibility
%A Liu, Bing
%A Scells, Harrisen
%A Hua, Wen
%A Zuccon, Guido
%A Zhao, Genghong
%A Zhang, Xia
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F liu-etal-2022-guiding
%X Entity Alignment (EA) aims to find equivalent entities between two Knowledge Graphs (KGs). While numerous neural EA models have been devised, they are mainly learned using labelled data only. In this work, we argue that different entities within one KG should have compatible counterparts in the other KG due to the potential dependencies among the entities. Making compatible predictions thus should be one of the goals of training an EA model along with fitting the labelled data: this aspect however is neglected in current methods. To power neural EA models with compatibility, we devise a training framework by addressing three problems: (1) how to measure the compatibility of an EA model; (2) how to inject the property of being compatible into an EA model; (3) how to optimise parameters of the compatibility model. Extensive experiments on widely-used datasets demonstrate the advantages of integrating compatibility within EA models. In fact, state-of-the-art neural EA models trained within our framework using just 5% of the labelled data can achieve comparable effectiveness with supervised training using 20% of the labelled data.
%R 10.18653/v1/2022.emnlp-main.32
%U https://aclanthology.org/2022.emnlp-main.32/
%U https://doi.org/10.18653/v1/2022.emnlp-main.32
%P 491-504
Markdown (Informal)
[Guiding Neural Entity Alignment with Compatibility](https://aclanthology.org/2022.emnlp-main.32/) (Liu et al., EMNLP 2022)
ACL
- Bing Liu, Harrisen Scells, Wen Hua, Guido Zuccon, Genghong Zhao, and Xia Zhang. 2022. Guiding Neural Entity Alignment with Compatibility. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 491–504, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.