Multi-granularity Histories Merging Network for Temporal Knowledge Graph Reasoning
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5618-6_14
Reference17 articles.
1. Jin, W., Qu, M., Jin, X., Ren, X.: Recurrent event network: autoregressive structure inferenceover temporal knowledge graphs. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 6669–6683. (2020)
2. Li, Y., Sun, S., Zhao, J.: TiRGN: time-guided recurrent graph network with local-global historical patterns for temporal knowledge graph reasoning. In: IJCAI, pp. 2152–2158 (2022)
3. Zhu, C., Chen, M., Fan, C., Cheng, G., Zhang, Y.: Learning from history: modeling temporal knowledge graphs with sequential copy-generation networks. In: Proceedings of the AAAI conference on artificial intelligence, pp. 4732–4740. (2020)
4. Li, Z., et al.: Complex evolutional pattern learning for temporal knowledge graph reasoning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 290–296 (2021)
5. Li, Z., et al.: Temporal knowledge graph reasoning based on evolutional representation learning. In: Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval, pp. 408–417 (2021)
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