Temporal knowledge graph reasoning based on relation graphs and time-guided attention mechanism
Author:
Funder
National Natural Science Foundation of China
Sichuan Province Science and Technology Support Program
Publisher
Elsevier BV
Reference45 articles.
1. A. Saxena, S. Chakrabarti, P. Talukdar, Question Answering Over Temporal Knowledge Graphs, in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021, pp. 6663–6676.
2. A. Saxena, A. Tripathi, P. Talukdar, Improving multi-hop question answering over knowledge graphs using knowledge base embeddings, in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020, pp. 4498–4507.
3. H. Wang, F. Zhang, J. Wang, M. Zhao, W. Li, X. Xie, M. Guo, RippleNet: Propagating user preferences on the knowledge graph for recommender systems, in: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018, pp. 417–426.
4. L. Wang, E.-P. Lim, Z. Liu, T. Zhao, Explanation guided contrastive learning for sequential recommendation, in: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022, pp. 2017–2027.
5. W. Jin, M. Qu, X. Jin, X. Ren, Recurrent Event Network: Autoregressive Structure Inferenceover Temporal Knowledge Graphs, in: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020, pp. 6669–6683.
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