A Temporal Knowledge Graph Embedding Model based on Additive Time Series DecompositionDownload PDF

Chengjin Xu, Mojtaba Nayyeri, Fouad Alkhoury, Hamed Shariat Yazdi, Jens Lehmann

22 May 2019 (modified: 27 May 2020)OpenReview Anonymous Preprint Blind SubmissionReaders: Everyone
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 remarkably outperforms the state-of-the-art KGE models and the existing temporal KGE models on link prediction over four temporal KGs.
Keywords: Temporal Knowledge Graph, Machine Learning, Embedding Model
TL;DR: Submitted to ISWC
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