Abstract
Embedding vertices and relations is the main direction and poses many challenges for the research community in link prediction on the knowledge graph. Among the state-of-the-art approaches, the approach based on the geometric transformations has the strong point of good intuition representation. Besides, the relations in the knowledge graph is diverse, so the embedding needs to be flexible. For that reason, we focus on analyzing the characteristics of the relations and the suitability for each transformation. As a result, we proposed a new method of embedding to capture better symmetric and composed relations. Furthermore, the self-adversarial sampling scheme is applied to reduce the influence of negative samples that do not carry meaningful information to the learning process. In addition, we also optimized the loss function by using the unlimited loss function. Experiments reflect the suggestions increasing the accuracy of the original model on the standard datasets.
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This research is funded by the Faculty of Information Technology, University of Science, VNU-HCM, Vietnam, Grant number CNTT 2021-03 and Advanced Program in Computer Science.
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Le, T., Huynh, N., Le, B. (2021). RotatHS: Rotation Embedding on the Hyperplane with Soft Constraints for Link Prediction on Knowledge Graph. In: Nguyen, N.T., Iliadis, L., Maglogiannis, I., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2021. Lecture Notes in Computer Science(), vol 12876. Springer, Cham. https://doi.org/10.1007/978-3-030-88081-1_3
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DOI: https://doi.org/10.1007/978-3-030-88081-1_3
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