Relation-attention semantic-correlative knowledge graph embedding for inductive link prediction
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Software
Link
https://link.springer.com/content/pdf/10.1007/s13042-023-01865-y.pdf
Reference43 articles.
1. Hwang JD, Bhagavatula C, Bras RL, Da J, Sakaguchi K, Bosselut A, Choi Y (2020) COMET-ATOMIC 2020: on symbolic and neural commonsense knowledge Graphs arXiv:2010.05953
2. Xun G, Jha K, Yuan Y, Zhang A (2019) Topic discovery for biomedical corpus using mesh embeddings. 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings, 2–5 . https://doi.org/10.1109/BHI.2019.8834559
3. Bosselut A, Bras RL, Choi Y (2019) Dynamic Neuro-Symbolic Knowledge Graph Construction for Zero-shot Commonsense Question Answering arXiv:1911.03876
4. Li J, Sun A, Guan Z, Cheema MA, Min G (2022) Real-time dynamic network learning for location inference modelling and computing. Neurocomputing 472:198–200. https://doi.org/10.1016/j.neucom.2021.10.086
5. Kotnis B, Lawrence C, Niepert M (2020) Answering complex queries in knowledge graphs with bidirectional sequence encoders arXiv:2004.02596
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