Abstract
In recent times, numerous studies on static knowledge graphs have achieved significant advancements. However, when extending knowledge graphs with temporal information, it poses a complex problem with larger data size, increased complexity in interactions between objects, and a potential for information overlap across time intervals. In this research, we introduce a novel model called TouriER, based on the MetaFormer architecture, to learn temporal features. We also apply a data preprocessing method to integrate temporal information in a reasonable manner. Additionally, the utilization of Fourier Transforms has proven effective in feature extraction. Through experiments on benchmark datasets, the TouriER model has demonstrated better performance compared to well-known models based on standard metrics.
This research is supported by the research funding from the Faculty of Information Technology, University of Science, Ho Chi Minh city, Vietnam.
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Vu, T., Ngo, H., Nguyen, NT., Le, T. (2023). TouriER: Temporal Knowledge Graph Completion by Leveraging Fourier Transforms. In: Honda, K., Le, B., Huynh, VN., Inuiguchi, M., Kohda, Y. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2023. Lecture Notes in Computer Science(), vol 14376. Springer, Cham. https://doi.org/10.1007/978-3-031-46781-3_7
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