Characterizing Evolutionary Trends in Temporal Knowledge Graphs with Linear Temporal Logic

Temporal changes in data set the need for the development of knowledge representation techniques able to capture these dynamics. Temporal Knowledge Graphs (TKGs) have emerged as a reference tool to represent dynamic, structured and machine-interpretable knowledge that evolves over time. However, characterizing the evolution of TKGs in a meaningful and human-understandable way remains a significant challenge. In this short paper, we argue that Linear Temporal Logic (LTL), with its expressive temporal constructs, is a promising approach for capturing a wide range of temporal behaviors and high-level evolutionary patterns occurring in TKGs.