Parameter-Lite Adapter for Dynamic Entity Alignment
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-7019-3_36
Reference28 articles.
1. Zhu, X., et al.: Multi-modal knowledge graph construction and application: a survey. IEEE Trans. Knowl. Data Eng. (2022)
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3. Yang, Y., Huang, C., Xia, L., Li, C.: Knowledge graph contrastive learning for recommendation, In: 2022 Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1434–1443 (2022)
4. Zhang, R., Trisedya, B.D., Li, M., Jiang, Y., Qi, J.: A benchmark and comprehensive survey on knowledge graph entity alignment via representation learning. VLDB J. 31(5), 1143–1168 (2022)
5. Guo, L., Han, Y., Zhang, Q., Chen, H.: Deep reinforcement learning for entity alignment. arXiv preprint arXiv:2203.03315 (2022)
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