A Multi-Granularity Implicit Temporal (MGIT) framework that enhances temporal representation and reasoning by modeling implicit temporal dependencies across different granularities is proposed, highlighting its effectiveness in capturing implicit temporal information and enhancing multi-granularity temporal reasoning.
The Temporal-Weighted Transfer Network (TWTNET), a novel reasoning model that jointly leverages statistical modeling and historical information transfer, is proposed, which achieves more robust and interpretable extrapolative reasoning.
Hongyu Hao, Yifan Zhang, Xinru Zhao et al.· ACM Transactions on Intellig...· 0 citations
SABET-QA is proposed, a framework that iteratively refines reasoning states across multiple hops via a bidirectional entity-temporal scoring mechanism and a slot-aware contextualization module that aligns question semantics with temporal KG embeddings.
Given a partially observed Temporal Knowledge Graph (TKG), how can we accurately predict missing entities? Unlike static knowledge graphs, TKGs encode facts within temporal contexts, requiring models to reason over both graph structure and time. However, existing TKGC approaches often sample neighbors solely based on t...
Seungjoo Lee, Yong-chan Park, U. Kang· Proceedings of the 32nd ACM...· 0 citations
Temporal knowledge graph (TKG) reasoning is critical for modeling and forecasting the evolution of real-world events. Existing TKG construction pipelines transform raw text into structured temporal quadruples as graph facts. However, in this process, they often fail to preserve reasoning-relevant contextual semantics f...
Ze-Shu Tian, J. Tao, Hong-Li Zhang· Proceedings of the Thirty-Fi...· 0 citations
FITTER consistently outperforms inductive baselines without retraining, indicating that vocabulary-agnostic structural learning is a viable foundation for inference over the heterogeneous knowledge graphs of the Semantic Web.
Jia-Xin Pan, M. Nayyeri, Osama Mohammed et al.· 0 citations
This work proposes a multi-granularity knowledge refinement approach to prune historical TKGs, which selectively removes irrelevant edges and unnecessary nodes at both the edge and node levels.
Fu-Wei Zhang, Fu-Zhen Zhuang, Zhao Zhang et al.· Frontiers of Computer Scienc...· 0 citations
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