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    <abstract>Temporal knowledge graph (TKG) reasoning, a central task in temporal knowledge representation, focuses on predicting future facts by leveraging historical temporal contexts. However, current approaches face two major challenges: limited generalization to unseen facts and insufficient interpretability of reasoning processes. To address these challenges, this paper proposes the **D**enoising **L**ogic-based **T**emporal **K**nowledge **G**raph (DLTKG) framework, which employs a denoising diffusion process to complete reasoning tasks by introducing a noise source and a historical conditionguiding mechanism. Specifically, DLTKG constructs fuzzy entity representations by treating historical facts as noise sources, thereby enhancing the semantic associations between entities and the generalization ability for unseen facts. Additionally, the condition-based guidance mechanism, rooted in the relationship evolutionary paths, is designed to improve the interpretability of the reasoning process. Furthermore, we introduce a fine-tuning strategy that optimizes the denoising process by leveraging shortest path information between the head entity and candidate entities. Experimental results on three benchmark datasets demonstrate that DLTKG outperforms state-of-the-art methods across multiple evaluation metrics. Our code is available at: https://github.com/NEU-IDKE/DLTKG</abstract>
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%0 Conference Proceedings
%T DLTKG: Denoising Logic-based Temporal Knowledge Graph Reasoning
%A Wang, Xiaoke
%A Zhang, Fu
%A Cheng, Jingwei
%A Chi, Yiwen
%A Peng, Jiashun
%A Ning, Yingsong
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F wang-etal-2025-dltkg
%X Temporal knowledge graph (TKG) reasoning, a central task in temporal knowledge representation, focuses on predicting future facts by leveraging historical temporal contexts. However, current approaches face two major challenges: limited generalization to unseen facts and insufficient interpretability of reasoning processes. To address these challenges, this paper proposes the **D**enoising **L**ogic-based **T**emporal **K**nowledge **G**raph (DLTKG) framework, which employs a denoising diffusion process to complete reasoning tasks by introducing a noise source and a historical conditionguiding mechanism. Specifically, DLTKG constructs fuzzy entity representations by treating historical facts as noise sources, thereby enhancing the semantic associations between entities and the generalization ability for unseen facts. Additionally, the condition-based guidance mechanism, rooted in the relationship evolutionary paths, is designed to improve the interpretability of the reasoning process. Furthermore, we introduce a fine-tuning strategy that optimizes the denoising process by leveraging shortest path information between the head entity and candidate entities. Experimental results on three benchmark datasets demonstrate that DLTKG outperforms state-of-the-art methods across multiple evaluation metrics. Our code is available at: https://github.com/NEU-IDKE/DLTKG
%R 10.18653/v1/2025.findings-emnlp.1017
%U https://aclanthology.org/2025.findings-emnlp.1017/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.1017
%P 18730-18743

Markdown (Informal)

[DLTKG: Denoising Logic-based Temporal Knowledge Graph Reasoning](https://aclanthology.org/2025.findings-emnlp.1017/) (Wang et al., Findings 2025)

• DLTKG: Denoising Logic-based Temporal Knowledge Graph Reasoning (Wang et al., Findings 2025)

ACL

• Xiaoke Wang, Fu Zhang, Jingwei Cheng, Yiwen Chi, Jiashun Peng, and Yingsong Ning. 2025. DLTKG: Denoising Logic-based Temporal Knowledge Graph Reasoning. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 18730–18743, Suzhou, China. Association for Computational Linguistics.