Cross-lingual Entity Alignment with Incidental Supervision

Muhao Chen, Weijia Shi, Ben Zhou, Dan Roth


Abstract
Much research effort has been put to multilingual knowledge graph (KG) embedding methods to address the entity alignment task, which seeks to match entities in different languagespecific KGs that refer to the same real-world object. Such methods are often hindered by the insufficiency of seed alignment provided between KGs. Therefore, we propose a new model, JEANS , which jointly represents multilingual KGs and text corpora in a shared embedding scheme, and seeks to improve entity alignment with incidental supervision signals from text. JEANS first deploys an entity grounding process to combine each KG with the monolingual text corpus. Then, two learning processes are conducted: (i) an embedding learning process to encode the KG and text of each language in one embedding space, and (ii) a self-learning based alignment learning process to iteratively induce the correspondence of entities and that of lexemes between embeddings. Experiments on benchmark datasets show that JEANS leads to promising improvement on entity alignment with incidental supervision, and significantly outperforms state-of-the-art methods that solely rely on internal information of KGs.
Anthology ID:
2021.eacl-main.53
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
645–658
Language:
URL:
https://aclanthology.org/2021.eacl-main.53
DOI:
10.18653/v1/2021.eacl-main.53
Bibkey:
Cite (ACL):
Muhao Chen, Weijia Shi, Ben Zhou, and Dan Roth. 2021. Cross-lingual Entity Alignment with Incidental Supervision. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 645–658, Online. Association for Computational Linguistics.
Cite (Informal):
Cross-lingual Entity Alignment with Incidental Supervision (Chen et al., EACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.eacl-main.53.pdf
Dataset:
 2021.eacl-main.53.Dataset.txt
Software:
 2021.eacl-main.53.Software.txt
Data
DBP15K