Temporal-structural importance weighted graph convolutional network for temporal knowledge graph completion

TL;DR: This study proposes ILR-IR, a hybrid model combining embedding-based and logical rule-based methods for temporal knowledge graph extrapolation, offering interpretable insights and superior performance in predicting future events with robust zero-shot reasoning abilities.

TL;DR: TransE is proposed, a method which models relationships by interpreting them as translations operating on the low-dimensional embeddings of the entities, which proves to be powerful since extensive experiments show that TransE significantly outperforms state-of-the-art methods in link prediction on two knowledge bases.