4 accepted papers
In recent years, Temporal Knowledge Graphs (TKGs) have developed rapidly and Temporal Knowledge Graph Completion (TKGC) aims at completing the missing parts of incomplete TKGs. Many methods have been proposed for TKGC, but there are still unresolved issues. Current methods have not fully explored th…
While large language models (LLMs) show great potential in temporal reasoning, most existing work focuses heavily on enhancing performance, often neglecting the explainable reasoning processes underlying the results. To address this gap, we introduce a comprehensive benchmark covering a wide range o…
Temporal Knowledge Graph Reasoning (TKGR) is the task of inferring missing facts for incomplete TKGs in complex scenarios (e.g., transductive and inductive settings), which has been gaining increasing attention. Recently, to mitigate dependence on structured connections in TKGs, text-based methods h…
Recently, self-supervised learning has attracted great attention, since it only requires unlabeled data for model training. Contrastive learning is one popular method for self-supervised learning and has achieved promising empirical performance. However, the theoretical understanding of its generali…