A knowledge graph embedding model based on multi-level analogical reasoning
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10586-024-04514-3.pdf
Reference35 articles.
1. Chen, M. Zhang, W., Zhu, Y., Zhou, H., Yuan, Z., Xu, C., Chen, H.: Meta-knowledge transfer for inductive knowledge graph embedding. In Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval (2022). https://doi.org/10.1145/3477495.3531757
2. Bevilacqua, M., Navigli, R.: Breaking through the 80% glass ceiling: Raising the state of the art in word sense disambiguation by incorporating knowledge graph information. Proceedings of the conference-Association for Computational Linguistics. Meeting. Association for Computational Linguistics (2020). https://doi.org/10.18653/v1/2020.acl-main.255
3. Yasunaga, M., Ren, H., Bosselut, A., Liang, P., Leskovec, J.: QA-GNN: Reasoning with language models and knowledge graphs for question answering. arXiv preprint arXiv:2104.06378 (2021).[cs.CL].
4. Hu, Z., Cao, Y., Huang, L., Chua, T. S.: How knowledge graph and attention help? A quantitative analysis into bag-level relation extraction. arXiv preprint arXiv:2107.12064 (2021).[cs.CL]. https://doi.org/10.18653/v1/2021.acl-long.359.
5. Dai, Y., Wang, S., Xiong, N.N., Guo, W.: A survey on knowledge graph embedding: approaches, applications and benchmarks. Electronics 9(5), 750 (2020). https://doi.org/10.3390/electronics9050750
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