Temporal Knowledge Graph Forecasting via Constrained Large Language Models

Conference Open access Sep 2026

Leveraging Implicit Contexts via LLM–Graph Fusion for Temporal Knowledge Graph Reasoning

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

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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 tunin...

Zi-Hao Jiang, Wen-Jie Xu, Miao Peng et al. · World wide web (Bussum) · 0 citations

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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.

Fu-Wei Zhang, Fu-Zhen Zhuang, Zhao Zhang et al. · Frontiers of Computer Scienc... · 0 citations

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#graph neural networks Oct 2026

LLM-RDO: Large Language Model-Guided Rules Dynamic Optimization for Temporal Knowledge Graph Reasoning

Temporal knowledge graph reasoning (TKGR) aims to predict future facts based on historical event facts. However, the traditional embedding-based methods lack interpretability and the rule-based methods are prone to falling into spurious correlation traps. Note that the recent large language model (LLM)-based methods ar...

Qin Liu, Yang-Yang Chen, Guang-Hui Wen · IEEE Internet of Things Jour... · 0 citations

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Preprint Aug 2026

DocTrace: Towards Traceable Long Document VQA via Hierarchical Evidence Graph Reasoning

This paper proposes DocTrace, a hierarchical framework that progressively performs evidence localization, structured document parsing, and evidence graph reasoning to enable explicit evidence provenance, and develops a two-stage training framework.

Lei Xiang, Zhi-Cheng Guan, Hong Chen et al. · 0 citations

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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.

Jia-Xin Pan, M. Nayyeri, Osama Mohammed et al. · 0 citations

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