Abstract
A Temporal Knowledge Graph (TKG) is designed for the effective modelling of the temporal relationships and dynamics of entities, events and concepts. Owing to its temporal attributes, a TKG offers greater benefits for reasoning than a static knowledge graph (KG). However, existing approaches for TKG reasoning do not consider coherent relationships between numerous facts, a term borrowed from specific parlance reflecting the interaction between objectives, such as observation and removal agents. This characteristic suggests that the model can obtain more insights from simultaneous coherent relationships. To address this problem, we develop a label-based process to construct a TKG from temporal event data in these domains. Based on the process, we build a specific TKG called Simulated Agent Interaction Knowledge Graph (SAIKG). In addition, we propose a novel TKG reasoning mechanism, termed the Coherence Mode. It is premised on an event coherence manner, enabling the prediction of unknown facts. Extensive experimental studies on different datasets demonstrate the effectiveness of the Coherence Mode integrated with typical models.
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