TL;DR
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.
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.
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.
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.
Knowledge graph completion (KGC) aims to infer missing facts based on existing triplets. Traditional path-based methods formulate reasoning as a discrete multi-hop traversal, which suffers from exponential search space and error accumulation.In this paper, we propose FlowPath, which rethinks the reasoning process by transforming discrete symbolic paths into a continuous semantic flow in the latent space. Specifically, we formalize the inference from a head entity to the target tail entity as an Ordinary Differential Equation (ODE) governed by a relation-conditioned neural velocity field, which learns to steer a smooth and expressive semantic transformation along the reasoning trajectory. To prevent the continuous flow from drifting into invalid semantic regions, we further introduce a structure-anchored guidance mechanism that leverages factual multi-hop paths from the knowledge graph as topological constraints, grounding the learned flow in the discrete relational structure. Moreover, a uncertainty weighting strategy is employed to adaptively balance the joint optimization of link prediction, flow generation, and structural guidance. By bridging discrete symbolic paths and continuous neural dynamics, FlowPath inherits the interpretability of path-based reasoning while possessing the flexibility and expressiveness of generative modeling. Extensive experiments on benchmark datasets demonstrate that FlowPath achieves competitive performance, and additional analysis confirms the effectiveness of modeling the reasoning paths of KGC as a continuous semantic flow.
To address the problems of heterogeneous structures, weak semantic alignment, sparse relations, and reasoning conflicts in multi-modal knowledge graph construction, this paper proposes a multi-modal data-driven knowledge graph reasoning model integrating unified encoding, graph representation learning, and rule constraints. Text descriptions, image labels, temporal attributes, and spatial coordinates are mapped into a shared representation space through modality-specific encoders and feature projection matrices. A multi-relational knowledge graph is then constructed through entity extraction, relation identification, triplet generation, and graph storage. On this basis, graph representation learning is introduced to aggregate neighborhood information and mine implicit relations among sparse nodes. To reduce semantic drift and invalid link prediction, temporal order, spatial inclusion, entity dependency, and event consistency rules are embedded into the reasoning score function. Candidate filtering and conflict resolution are further designed to improve inference efficiency under large-scale triplet conditions. Experimental results show that the proposed model achieves 0.894 Precision, 0.872 Recall, 0.883 F1, 0.907 MRR, and 0.931 Hits@10, outperforming TransE, GCN, GAT, R-GCN, and CompGCN. When the candidate triplet scale reaches 10,000, the inference response time of the proposed model is 146 ms, indicating better relation completion accuracy, reasoning stability, and inference efficiency in multi-modal knowledge graph reasoning tasks.