Block term decomposition with distinct time granularities for temporal knowledge graph completion

Abstract

To solve the incompleteness problem in temporal knowledge graphs (TKGs) and to discover the new knowledge, TKG completion remains an essential task always solved by graph embedding technology. Existing TKG completion methods encode time at only single granularity, which is insufficient in exploiting the rich information of distinct time granularities. Furthermore, most of them lack a comprehensive consideration of the characteristic of both time points and time periods, resulting in the inability to handle the two types of facts with different time forms, namely the discrete facts and continuous facts, simultaneously. In this paper, we propose a novel TKG embedding model which introduces the block term tensor decomposition and utilizes the core tensor and factor matrices to capture information presented by facts under distinct time granularities. By focusing on moments included in the time period and treating the discrete fact as a special case of the continuous fact, the model manages the processing of different types of facts in a unified manner. Besides, we explicitly design the static properties of entities and relations as well as their interactions to conform to reality. Experiments on 3 real datasets of different types verify the effectiveness of our proposed method compared with most state-of-the-art methods.