Abstract
Predicting facts that occur in the future is a challenging task in temporal knowledge graphs (TKGs). TKGs represent temporal facts about entities and their relations, where each fact is associated with a timestamp. Inspired from the human inference process that predictions are usually made by analyzing relevant historical clues, in this paper, we propose a model based on temporal evolution and temporal graph attention mechanism to infer future facts. Specifically, we construct a node pool to keep the importance of all nodes encountered in the historical search. We learn temporal evolution features and sub-graph structures based on temporal random walks and graph attention networks. Moreover, these sub-graphs are sets of objects with the same subjects and relations as the query. Experiments on five temporal datasets demonstrate the effectiveness of the model compared with the state-of-the-art methods. Codes are available at https://github.com/lendie/SWGAT.
H. Tang and D. Liu—Equal contribution.
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Acknowledgment
This work is supported by grants from Shengze Li’s National Natural Science Foundation of China (No. 11901578).
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Tang, H., Liu, D., Xu, X., Zhang, F. (2023). Evolving Temporal Knowledge Graphs by Iterative Spatio-Temporal Walks. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1791. Springer, Singapore. https://doi.org/10.1007/978-981-99-1639-9_42
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