Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network

Kun Xu, Liwei Wang, Mo Yu, Yansong Feng, Yan Song, Zhiguo Wang, Dong Yu


Abstract
Previous cross-lingual knowledge graph (KG) alignment studies rely on entity embeddings derived only from monolingual KG structural information, which may fail at matching entities that have different facts in two KGs. In this paper, we introduce the topic entity graph, a local sub-graph of an entity, to represent entities with their contextual information in KG. From this view, the KB-alignment task can be formulated as a graph matching problem; and we further propose a graph-attention based solution, which first matches all entities in two topic entity graphs, and then jointly model the local matching information to derive a graph-level matching vector. Experiments show that our model outperforms previous state-of-the-art methods by a large margin.
Anthology ID:
P19-1304
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3156–3161
Language:
URL:
https://aclanthology.org/P19-1304
DOI:
10.18653/v1/P19-1304
Bibkey:
Cite (ACL):
Kun Xu, Liwei Wang, Mo Yu, Yansong Feng, Yan Song, Zhiguo Wang, and Dong Yu. 2019. Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3156–3161, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network (Xu et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1304.pdf
Code
 syxu828/Crosslingula-KG-Matching +  additional community code
Data
DBpedia