Abstract
Knowledge Graph (KG) embedding has attracted more attention in recent years. Most KG embedding models learn from time-unaware triples. However, the inclusion of temporal information besides triples would further improve the performance of a KGE model. In this regard, we propose ATiSE, a temporal KG embedding model which incorporates time information into entity/relation representations by using Additive Time Series decomposition. Moreover, considering the temporal uncertainty during the evolution of entity/relation representations over time, we map the representations of temporal KGs into the space of multi-dimensional Gaussian distributions. The mean of each entity/relation embedding at a time step shows the current expected position, whereas its covariance (which is temporally stationary) represents its temporal uncertainty. Experimental results show that ATiSE significantly outperforms the state-of-the-art KGE models and the existing temporal KGE models on link prediction over four temporal KGs.
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Notes
- 1.
The code is available at https://github.com/soledad921/ATISE.
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Acknowledgements
This work is supported by the CLEOPATRA project (GA no. 812997), the German national funded BmBF project MLwin and the BOOST project.
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Xu, C., Nayyeri, M., Alkhoury, F., Yazdi, H., Lehmann, J. (2020). Temporal Knowledge Graph Completion Based on Time Series Gaussian Embedding. In: Pan, J.Z., et al. The Semantic Web – ISWC 2020. ISWC 2020. Lecture Notes in Computer Science(), vol 12506. Springer, Cham. https://doi.org/10.1007/978-3-030-62419-4_37
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