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arXiv · 1911.07893

Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition

Chengjin Xu·Mojtaba Nayyeri·Fouad Alkhoury·Hamed Shariat Yazdi·Jens Lehmann
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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 beside 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 chieves the state-of-the-art on link prediction over four temporal KGs.

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Chengjin Xu, Mojtaba Nayyeri, Fouad Alkhoury, Hamed Shariat Yazdi, Jens Lehmann. 2019-11-18. Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition. https://arxiv.org/abs/1911.07893

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