Improving Inductive Link Prediction Using Hyper-relational Facts

Author:

Ali Mehdi,Berrendorf Max,Galkin Mikhail,Thost Veronika,Ma Tengfei,Tresp Volker,Lehmann Jens

Publisher

Springer International Publishing

Reference29 articles.

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2. Ali, M., et al.: Bringing light into the dark: a large-scale evaluation of knowledge graph embedding models under a unified framework. CoRR arXiv:2006.13365 (2020)

3. Ali, M., et al.: PyKEEN 1.0: a python library for training and evaluating knowledge graph embeddings. J. Mach. Learn. Res. 22(82), 1–6 (2021). http://jmlr.org/papers/v22/20-825.html

4. Baek, J., Lee, D.B., Hwang, S.J.: Learning to extrapolate knowledge: Transductive few-shot out-of-graph link prediction. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, 6–12 December 2020 (2020). Virtual

5. Bagherian, M., Sabeti, E., Wang, K., Sartor, M.A., Nikolovska-Coleska, Z., Najarian, K.: Machine learning approaches and databases for prediction of drug-target interaction: a survey paper. Brief. Bioinform. 22(1), 247–269 (2020). https://doi.org/10.1093/bib/bbz157

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