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On the Knowledge Graph Completion Using Translation Based Embedding: The Loss Is as Important as the Score

M. Nayyeri, C. Xu, Y. Yaghoobzadeh, H. Yazdi, and J. Lehmann. ArXiv, (2019)

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Fantastic knowledge graph embeddings and how to find the right space for themM. Nayyeri, C. Xu, S. Vahdati, N. Vassilyeva, E. Sallinger, H. Yazdi, and J. Lehmann. Lecture Notes in Computer Science, Springer International Publishing, Cham, (2020)On the Knowledge Graph Completion Using Translation Based Embedding: The Loss Is as Important as the ScoreM. Nayyeri, C. Xu, Y. Yaghoobzadeh, H. Yazdi, and J. Lehmann. ArXiv, (2019)TeRo: A Time-aware Knowledge Graph Embedding via Temporal RotationC. Xu, M. Nayyeri, F. Alkhoury, H. Yazdi, and J. Lehmann. Proceedings of the 28th International Conference on Computational Linguistics, International Committee on Computational Linguistics, (2020)Knowledge graph embeddings in geometric algebrasC. Xu, M. Nayyeri, Y. Chen, and J. Lehmann. (2020)LogicENN: A neural based knowledge graphs embedding model with logical rulesM. Nayyeri, C. Xu, J. Lehmann, and H. Yazdi. (2019)Temporal knowledge Graph embedding model based on Additive Time Series decompositionC. Xu, M. Nayyeri, F. Alkhoury, H. Yazdi, and J. Lehmann. (2019)

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On the Knowledge Graph Completion Using Translation Based Embedding: The Loss Is as Important as the Score

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On the Knowledge Graph Completion Using Translation Based Embedding: The Loss Is as Important as the Score