Link prediction for knowledge graphs based on extended relational graph attention networks

Author:

Cao Zhanyue,Luo Chao

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shandong Province

Publisher

Elsevier BV

Reference39 articles.

1. Atif, F., El Khatib, O., & Difallah, D. (2023). Beamqa: Multi-hop knowledge graph question answering with sequence-to-sequence prediction and beam search. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 781–790).

2. Self-attention presents low-dimensional knowledge graph embeddings for link prediction;Baghershahi;Knowledge-Based Systems,2023

3. Translating embeddings for modeling multi-relational data;Bordes;Advances in Neural Information Processing Systems,2013

4. Bouchard, G., Singh, S., & Trouillon, T. (2015, March). On approximate reasoning capabilities of low-rank vector spaces. In 2015 AAAI Spring Symposium Series.

5. Knowledge graph embedding: A survey from the perspective of representation spaces;Cao;ACM Computing Surveys,2024

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