Leveraging semantic property for temporal knowledge graph completion
Author:
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-022-03981-8.pdf
Reference40 articles.
1. Mezni H (2021) Temporal knowledge graph embedding for effective service recommendation. IEEE Transactions on Services Computing. https://doi.org/10.1109/TSC.2021.3075053
2. Bai L, Yu W, Chen M, Ma X (2021) Multi-hop reasoning over paths in temporal knowledge graphs using reinforcement learning. Applied Soft Computing. https://doi.org/10.1016/j.asoc.2021.107144https://doi.org/10.1016/j.asoc.2021.107144
3. Deng S, Rangwala H, Ning Y (2020) Dynamic knowledge graph based multi-event forecasting. In: Proceedings of the 26th ACM SIGKDD. https://doi.org/10.1145/3394486.3403209https://doi.org/10.1145/3394486.3403209. ACM, New York, pp 1585–1595
4. Kazemi SM, Goel R, Jain K (2020) Representation learning for dynamic graphs: a survey. Journal of Machine Learning Research
5. Wu J, Cao M, Cheung JCK (2020) TeMP: temporal message passing for temporal knowledge graph completion. In: Proceedings of EMNLP
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. CACL:Commonsense-Aware Contrastive Learning for Knowledge Graph Completion;Communications in Computer and Information Science;2023-11-27
2. GLANet: temporal knowledge graph completion based on global and local information-aware network;Applied Intelligence;2023-03-02
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