An unsupervised multi-view contrastive learning framework with attention-based reranking strategy for entity alignment

Author:

Liang Yan,Cai WeishanORCID,Yang Minghao,Jiang YunchengORCID

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference59 articles.

1. Bordes, A., Usunier, N., García-Durán, A., Weston, J., & Yakhnenko, O. (2013). Translating embeddings for modeling multi-relational data. In NeurIPS (pp. 2787–2795).

2. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., et al. (2020). Language models are few-shot learners. In NeurIPS.

3. Semi-supervised entity alignment via relation-based adaptive neighborhood matching;Cai;TKDE,2023

4. Cai, W., Ma, W., Zhan, J., & Jiang, Y. (2022). Entity alignment with reliable path reasoning and relation-aware heterogeneous graph transformer. In IJCAI (pp. 1930–1937).

5. Cai, L., Mao, X., Ma, M., Yuan, H., Zhu, J., & Lan, M. (2022). A simple temporal information matching mechanism for entity alignment between temporal knowledge graphs. In COLING (pp. 2075–2086).

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