BGAT-CCRF: A novel end-to-end model for knowledge graph noise correction

Author:

Ma JiangtaoORCID,Li Kunlin,Zhang Fan,Wang Yanjun,Luo Xiangyang,Li Chenliang,Qiao Yaqiong

Funder

Zhengzhou University of Light Industry

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference48 articles.

1. Entity alignment based on relational semantics augmentation for multilingual knowledge graphs;Akhtar;Knowledge-Based Systems,2022

2. Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., & Yakhnenko, O. (2013). Translating Embeddings for Modeling Multi-relational Data. In Advances in neural information processing systems 26: 27th annual conference on neural information processing systems 2013 (pp. 2787–2795).

3. Brody, S., Alon, U., & Yahav, E. (2022). How attentive are graph attention networks?. In International conference on learning representations.

4. Parame: Regarding neural network parameters as relation embeddings for knowledge graph completion;Che,2020

5. Chen, J., He, H., Wu, F., & Wang, J. (2021). Topology-aware correlations between relations for inductive link prediction in knowledge graphs. In Proceedings of the 35th AAAI conference on artificial intelligence (pp. 6271–6278).

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