DLTKG : Denoising Logic-based Temporal Knowledge Graph Reasoning (2025)

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

## introduction the temporal knowledge graph (tkg) @xcite @xcite is a dynamic multirelational graph structure represented in the form of quadruples (s, r, o, t), where s denotes the subject (i.e., head entity), r denotes the relation, o denotes the object (i.e., tail entity), and t denotes the timestamp. the reasoning tasks of tkgs are primarily divided into interpolation and extrapolation @xcite . the interpolation task @xcite involves inferring missing facts within a known time interval, while the extrapolation task @xcite @xcite focuses on predicting future events. this study specifically addresses the extrapolation task of tkgs, as it can forecast future events and provide forward-looking insights for decision-making, offering substantial practical value in areas such as event prediction @xcite , risk prediction @xcite , and trend analysis @xcite . recent studies @xcite suggest that during narrative comprehension, humans activate neural representations of relevant historical events at event boundaries, facilitated by the hippocampus and default mode network. this enables memory integration and knowledge structure updates across time scales. as shown in figure 1 (a), humans follow a threestep process in prediction tasks: recalling historical events, filtering potential answers, and combining personal experience to form a prediction. figure 1 (b) illustrates tkg extrapolation, where the task is to predict whom obama visited on december 29, 2014. the process begins with recalling events related to "make_a_visit" and "barack_obama", filtering outcomes like malaysia, china, poland, and concluding that obama visited malaysia. tkg reasoning has seen notable progress in recent years @xcite @xcite ), as will be discussed in section 2. motivated by the success of diffusion techniques in sequence-to-sequence tasks @xcite , diffutkg @xcite introduces diffusion methods to tkg reasoning, achieving competitive results. however, existing diffusion models @xcite lack interpretability, as random noise and denoising are disconnected from the target entities. to address this, we propose a noise-adding and denoising method based on historical evolutionary paths (heps), leveraging past entities to generate fuzzy entities, i.e., the memory fusion process. denoising these fuzzy entities enhances the interpretability and generalization of the reasoning process. specifically, we propose three innovative strategies: (1) sequence learning strategy: this module extracts the heps of each relationship and utilizes them as denoising conditional guiding information. this design enables dltkg to perform efficient logical reasoning based on heps. (2) entity fusion strategy: this strategy merges entities that have appeared in heps as memory information to obtain noisy fuzzy entities. given the strong semantic and structural correlations between historical and target entities, key information about the target entity may be implicitly embedded within the historical entities. consequently, generating noise through the fusion of historical entities is more justifiable. (3) fine-tuning strategy: after the initial round of denoising, candidate entities are ranked from high to low based on their scores, and the top k entities are selected. the shortest paths between the query head entity and the top k candidate entities are then obtained, and these shortest paths are used as guiding information for further denoising. empirical research on three benchmark datasets validates the effectiveness of dltkg. the main contributions are as follows: ## problem formulation a temporal knowledge graph @xmath0, r, t , q) @xcite is a directed multirelational graph where there are timestamped edges between entities, with e, r, and t representing the sets of entities, relations, and timestamps, respectively. the problem addressed in this paper is temporal knowledge graph reasoning (tkgr) through extrapolation, which is formalized as link prediction aimed at inferring future quadruples. formally, for a quadruple query (s q , r q , ?, t q ), the goal of extrapolated tkgr is to predict the missing entity o q , given the historical graph sequence g 1 , g 2 , . . . , g tq-1 prior to the prediction time t q . ## method the dltkg framework (figure 2 ) consists of several key components: (1) sequence learning, which helps denoise by extracting relationship evolutionary patterns as conditional information; (2) entity fusion strategy, which integrates historical information related to the target entity to obtain a fuzzy entity, modeling the potential uncertainty between entities; (3) fine-tuning strategy, which uses the top k candidate entities and the query head entity s q to calculate shortest paths, providing conditional information for further training. ## conclusion in this paper, we present dltkg, a temporal knowledge graph reasoning model based on a denoising diffusion process for future fact prediction. we introduce an entity fusion strategy that aggregates past entities into fuzzy representations, reconstructed by a conditional denoising decoder. the fine-tuning phase further refines the model by incor-porating the shortest paths between query head entity and candidate entities as additional conditions. empirical results on benchmark datasets demonstrate that dltkg outperforms existing methods, offering superior accuracy and generalization in temporal reasoning tasks.