Abstract
The large-scale exploitation of knowledge graphs has promoted research efforts on graph construction and completion in many organizations such as Google, Apple. The problem of predicting the missing links in the knowledge graph often depends heavily on the method of embedding the vertices into a low-dimensional space, mostly considering the relations as a translation. Recently, there is an approach based on rotation embedding, which can improve efficiency remarkably. Therefore, in this paper, we propose an approach towards rotation embedding entities on a low-dimensional vector. Specifically, we start by projecting the entities onto the relation-specific hyperplanes before rotating them so that the head entities are as close as possible to the tail entities. Based on that, each relation is a rotation from the head entities to the tail entities on the hyperplane in complex vector space. Experiments on well-known datasets show the improvement of the proposed model compared to other models.
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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). Link Prediction on Knowledge Graph by Rotation Embedding on the Hyperplane in the Complex Vector Space. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2021. ICANN 2021. Lecture Notes in Computer Science(), vol 12893. Springer, Cham. https://doi.org/10.1007/978-3-030-86365-4_14
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