The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Bootstrapping Entity Alignment with Knowledge Graph Embedding
Open accessAbstract
Embedding-based entity alignment represents different knowledge graphs (KGs) as low-dimensional embeddings and finds entity alignment by measuring the similarities between entity embeddings. Existing approaches have achieved promising results, however, they are still challenged by the lack of enough prior alignment as labeled training data. In this paper, we propose a bootstrapping approach to embedding-based entity alignment. It iteratively labels likely entity alignment as training data for learning alignment-oriented KG embeddings. Furthermore, it employs an alignment editing method to reduce error accumulation during iterations. Our experiments on real-world datasets showed that the proposed approach significantly outperformed the state-of-the-art embedding-based ones for entity alignment. The proposed alignment-oriented KG embedding, bootstrapping process and alignment editing method all contributed to the performance improvement.
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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
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Convolutional 2D Knowledge Graph Embeddings
Dettmers, T., Minervini, P., Stenetorp, P., & Riedel, S. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11573 · Cited by 2,486
Unsupervised word sense disambiguation rivaling supervised methods
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A Three-Way Model for Collective Learning on Multi-Relational Data
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Reasoning With Neural Tensor Networks for Knowledge Base Completion
Socher, R., Chen, D., Manning, C. D., & Ng, A. Y. (2013) · Cited by 1,667
A multilevel algorithm for partitioning graphs
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Modeling Relation Paths for Representation Learning of Knowledge Bases
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Cited by
A Survey on Knowledge Graphs: Representation, Acquisition, and Applications
Ji, S., Pan, S., Cambria, E., Marttinen, P., & Yu, P. S. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3070843 · Cited by 2,947
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
A benchmarking study of embedding-based entity alignment for knowledge graphs
Sun, Z., Zhang, Q., Hu, W., Wang, C., Chen, M., Akrami, F., & Li, C. (2020) · Proceedings of the VLDB Endowment · doi:10.14778/3407790.3407828 · Cited by 265
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Metadata
- Authors
- Sun, Z., Hu, W., Zhang, Q., & Qu, Y.
- Venue
- Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
- Published
- 2018-07
- Pages
- 4396-4402
- OpenAlex
- W2808284704
- Cited by
- 521
- References
- 19
- FWCI
- 19.66
- Type
- conference paper
- Language
- en
- Open access
- bronze
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