Explainable Link Prediction for Emerging Entities in Knowledge Graphs

Author:

Bhowmik Rajarshi,de Melo Gerard

Publisher

Springer International Publishing

Reference47 articles.

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2. Bhowmik, R., de Melo, G.: Be concise and precise: synthesizing open-domain entity descriptions from facts. In: Proceedings of The Web Conference 2019, pp. 116–126. ACM, New York (2019)

3. Bordes, A., Usunier, N., García-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. Adv. Neural Inform. Process. Syst. 26, 2787–2795 (2013)

4. Das, R., et al.: Go for a walk and arrive at the answer: reasoning over paths in knowledge bases using reinforcement learning. arXiv 1711.05851 (2017)

5. Dasgupta, S.S., Ray, S.N., Talukdar, P.: HyTE: hyperplane-based temporally aware knowledge graph embedding. In: Proceedings of EMNLP 2018 (2018)

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