Relation representation based on private and shared features for adaptive few-shot link prediction
Author:
Funder
Guangdong Basic and Applied Basic Research Foundation
Top Youth Talent Project of Zhujiang Talent Program
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10844-024-00856-x.pdf
Reference46 articles.
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2. Bordes, A., Usunier, N., Garcia-Duran, A., et al. (2013). Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, (pp. 2787–2795). https://doi.org/10.5555/2999792.2999923
3. Cai, L., Wang, L., Yuan, R., et al. (2023). Meta-learning based dynamic adaptive relation learning for few-shot knowledge graph completion. Big Data Research, 33(100), 394. https://doi.org/10.1016/J.BDR.2023.100394
4. Carlson, A., Betteridge, J., Kisiel, B., et al. (2010). Toward an architecture for never-ending language learning. In: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, (pp. 1306–1313). https://doi.org/10.1609/AAAI.V24I1.7519
5. Chen, M., Zhang, W., Zhang, W., et al. (2019). Meta relational learning for few-shot link prediction in knowledge graphs. In: Conference on Empirical Methods in Natural Language Processing, (pp. 4217–4226). https://doi.org/10.18653/V1/D19-1431
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