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Aug 2026

Structure-aware generative framework for temporal knowledge graph reasoning with historical evidence

AG is proposed, a Structure-Aware Generative framework for temporal knowledge graph reasoning with historical evidence that first constructs dual-view historical evidence to better capture structural dependencies in temporal knowledge graphs and formulates TKGR as an end-to-end generative task through instruction tuning.

Zihao Jiang, Wenjie Xu, Miao Peng et al. · 0 citations
Aug 2026

Temporal knowledge graph reasoning via multi-granularity knowledge refinement

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.

Fuwei Zhang, Fuzhen Zhuang, Zhao Zhang et al. · 0 citations
Preprint Aug 2026

FITTER: Vocabulary-Agnostic Cross-Domain Inference on Temporal Knowledge Graphs

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.

Jiaxin Pan, M. Nayyeri, Osama Mohammed et al. · 0 citations
Aug 2026

MGIT: Multi-Granularity implicit temporal framework for knowledge graph question answering

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.

Jixiang Fang, Ling Lu, Xiaoyang Liu · 0 citations
Jul 2026

Temporal-Weighted Transfer Network for Knowledge Graph Extrapolation

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. · 0 citations
Conference Open access 2026

ODL-TempLLM: Ontology-Guided and Description Logic-Reasoned Temporal Reasoning with LLMs

ODL-TempLLM leverages ontology learning to explicitly construct structured temporal knowledge, employs a symbolic reasoner to deductively reason about temporal relations and uses logic-constrained retrieval augmentation to obtain relevant facts.

Jinshuo Liu, Cheng Bi, Meng Wang et al. · 0 citations