Complex expressional characterizations learning based on block decomposition for temporal knowledge graph completion

Abstract

A temporal knowledge graph (TKG) is a set of facts associated with different timestamps. TKG completion (TKGC) is the task of inferring unknown facts based on known facts, where mining and understanding the expressional characterization of timestamps from the fact sets are key. Expressional characterizations are complex because of two aspects: type-diversity and dependence-variability. Existing approaches do not comprehensively consider time points and time intervals; hence, facts with different time forms cannot be managed simultaneously. Additionally, these approaches overlook the interdependence and variability of timestamp positions in different types of facts, such as the relative order (e.g., whether the facts belong to the past, present, or future) and relative distance (e.g., the largest distance at which the facts occur), thus hindering the adequate perception of position. Therefore, we propose a novel TKGC model named Complex Expressional Characterizations with Block Decomposition (CEC-BD). This model is based on the block decomposition model and exploits sophisticated interactions for different types of facts by unifying the treatment of both time points and time intervals. In addition, we introduce a time-sensitive approach that injects timestamps with embedded information regarding fact occurrences into the CEC-BD model to capture the relative order and distance. To cluster and order time embeddings more effectively, we add a bias component that is randomly initialized and learned during training and further propose a temporal smoothness scheme. In particular, the model achieved a mean reciprocal rank of 3.05% and 10 times the speedup of state-of-the-art baseline models.