The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks
Open accessAbstract
Multilingual knowledge graphs (KGs) such as DBpedia and YAGO contain structured knowledge of entities in several distinct languages, and they are useful resources for cross-lingual AI and NLP applications.Cross-lingual KG alignment is the task of matching entities with their counterparts in different languages, which is an important way to enrich the crosslingual links in multilingual KGs.In this paper, we propose a novel approach for crosslingual KG alignment via graph convolutional networks (GCNs).Given a set of pre-aligned entities, our approach trains GCNs to embed entities of each language into a unified vector space.Entity alignments are discovered based on the distances between entities in the embedding space.Embeddings can be learned from both the structural and attribute information of entities, and the results of structure embedding and attribute embedding are combined to get accurate alignments.In the experiments on aligning real multilingual KGs, our approach gets the best performance compared with other embedding-based KG alignment approaches.
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Veličković, P., Casanova, A., Liò, P., Cucurull, G., Romero, A., & Bengio, Y. (2017) · arXiv (Cornell University) · doi:10.17863/cam.48429 · Cited by 8,657
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Modeling Relational Data with Graph Convolutional Networks
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Advances In Deep Learning On Graphs (Gsp'18 Workshop)
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Spectral Networks and Locally Connected Networks on Graphs
Bruna, J., Zaremba, W., Szlam, A., & LeCun, Y. (2013) · arXiv · doi:10.48550/arxiv.1312.6203 · Cited by 3,983
Knowledge Graph Embedding by Translating on Hyperplanes
Wang, Z., Zhang, J., Feng, J., & Chen, Z. (2014) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v28i1.8870 · Cited by 3,915
Self-citationLearning Entity and Relation Embeddings for Knowledge Graph Completion
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DBpedia - A crystallization point for the Web of Data
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Cited by
Graph neural networks: A review of methods and applications
Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2020) · AI Open · doi:10.1016/j.aiopen.2021.01.001 · Cited by 6,880
A Comprehensive Survey on Automatic Knowledge Graph Construction
Zhong, L., Wu, J., Li, Q., Peng, H., & Wu, X. (2024) · ACM Computing Surveys · doi:10.1145/3618295 · Cited by 382
Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network
Xu, K., Wang, L., Yu, M., Feng, Y., Song, Y., Wang, Z., & Yu, D. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1304 · Cited by 253
Self-citationNode Masking: Making Graph Neural Networks Generalize and Scale Better
Mishra, P., Piktus, A., Goossen, G., & Silvestri, F. (2020) · arXiv · doi:10.48550/arxiv.2001.07524 · Cited by 8
One-shot Learning for Temporal Knowledge Graphs
Mirtaheri, M., Rostami, M., Ren, X., Morstatter, F., & Galstyan, A. (2020) · arXiv · doi:10.48550/arxiv.2010.12144
Metadata
- Authors
- Wang, Z., Lv, Q., Lan, X., & Zhang, Y.
- Venue
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Published
- 2018
- Pages
- 349-357
- OpenAlex
- W2890187992
- Cited by
- 622
- References
- 21
- FWCI
- 23.41
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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