Abstract
Knowledge is increasingly completed due to connections formed in a knowledge graph, enabling a complete understanding of reality. Link prediction plays an important role in this process. Among the multiple methods that exist to tackle this problem, the geometry-based prediction method has attracted attention due to its intuitiveness and capacity to flexibly address various types of relations. We propose the rotation embedding of entities on separate relation-specific hyperplanes as an alternative to the translation method. Moreover, instead of optimizing the model by tight constraints, we add several soft constraints to minimize the loss function. Experimenting on standard datasets with numerous evaluation metrics, the proposed model outperforms both state-of-the-art and baseline models. We also analyze the model on multiple batch sizes and negative sample size values, along with various embedding dimensions and optimizers. Thereby, we demonstrate the impact of the parameters on the geometry-based link prediction model and provide a basis for future improvement.
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This work is supported by the Faculty of Information Technology, University of Science, VNU-HCM, Vietnam under grant number CNTT 2022-02.
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Le, T., Huynh, N. & Le, B. Knowledge graph embedding by projection and rotation on hyperplanes for link prediction. Appl Intell 53, 10340–10364 (2023). https://doi.org/10.1007/s10489-022-03983-6
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DOI: https://doi.org/10.1007/s10489-022-03983-6