FlowPath: From Discrete Paths to Continuous Semantic Flows for Knowledge Graph Completion

Knowledge Graph Completion Model Based on Semantic Constrained Hybrid Scoring

Knowledge Graph Completion (KGC) aims to predict missing links in knowledge bases. However, existing methods often struggle with reasoning over complex semantic relations and generate predictions that violate common-sense logic. In this paper, a knowledge graph completion model based on semantic constrained hybrid scoring (SCHS) is proposed, which integrates three synergistic mechanisms. First, an Adaptive Feature Gating (AFG) mechanism is designed for feature recalibration. Unlike static embeddings, this module dynamically recalibrates the importance of different semantic aspects based on the relation context. Second, a Relation-Concept Matrix Projection (RCMP) mechanism is introduced to act as an explicit semantic constraint. This mechanism maps triplets to a latent concept space to capture the inherent consistency between relations and concepts, providing a penalization mechanism to filter out noise that is topologically close but semantically invalid. Third, an Adaptive Fusion Strategy (AFS) utilizing a soft-start mechanism is introduced to effectively balance structural and semantic scores without early-training collapses. This tri-fold synergy essentially resolves the rigid trade-off between geometric alignment and flexible semantic matching. Extensive experiments on the WN18RR and FB15k-237 datasets demonstrate that SCHS achieves substantial performance improvements, verifying the robustness and effectiveness of the dynamically regulated fusion of semantics and structures in the alleviation of long-tail errors.

Multi-curvature progressive fusion for knowledge graph completion

Knowledge graphs have become a fundamental representation for structured knowledge, yet their incompleteness remains a major obstacle to reliable reasoning. Missing links may interrupt relational paths and limit the evidence available for downstream inference. Knowledge graph completion addresses this problem by predicting missing facts from observed relational patterns. Existing methods have made notable progress, but many of them rely on a single geometric space or fuse multiple geometric representations in a static manner. This limits their ability to capture heterogeneous graph structures, especially when hierarchical dependencies, local relational patterns, and cyclic motifs coexist in the same knowledge graph. In this paper, we propose multi-curvature progressive fusion (MCPF), a geometry-aware graph neural reasoning framework for knowledge graph completion. MCPF learns relation-aware representations in Euclidean, hyperbolic, and hyperspherical spaces, which provide complementary inductive biases for conventional relational interactions, hierarchical organization, and cyclic structures. Instead of directly aggregating these representations, MCPF progressively refines them through cross-space interactions and uses a query-conditioned gating mechanism to determine their relative contributions. This design enables the model to emphasize reliable geometric signals while suppressing less informative ones for each query. The fused representation is then used within a graph neural reasoning architecture for message passing and link prediction. Experiments on four benchmark datasets demonstrate that MCPF achieves competitive results against representative baselines and consistently outperforms its ablated variants.

PEARL: Path-Entity Aligned Relational Learning with Contextual Subgraphs for Inductive Knowledge Graph Completion

Inductive knowledge graph completion (IKGC) aims to predict missing links involving entities unseen during training, requiring models to learn transferable relational and structural patterns. Existing subgraph- and path-based approaches often encode relational paths independently of their surrounding query subgraphs, although the predictive relevance of a path may vary across structural contexts. We propose PEARL, a Path-Entity Aligned Relational Learning framework that models paths as context-conditioned reasoning signals. PEARL constructs a query-specific contextual subgraph from the union of the query entities'neighborhoods and uses a large language model (LLM)-guided retriever to distill semantically relevant paths. It then builds a bipartite interaction graph over paths, contextual entities, and a global subgraph representation, allowing path embeddings to adapt to local and global structural evidence. To suppress noise introduced by the enlarged context, PEARL employs a dual-view contrastive objective that promotes representation consistency under stochastic contextual perturbations. Experiments on WN18RR, FB15k-237, and NELL-995 show that PEARL obtains the best average Hits@10 among the compared IKGC methods on all three benchmarks. Ablation studies, efficiency analyses, and case studies further validate the contributions of contextual subgraph modeling, semantic path retrieval, path-entity interaction, and contrastive regularization.

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

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.

A Dual-Level Structural Context Collaborative Framework for Knowledge Graph Completion

: Knowledge graphs organize real-world facts as structured triples and have become a fundamental resource for search engines, question answering, recommender systems, and knowledge-enhanced large language models. However, real-world knowledge graphs remain highly incomplete, which limits their downstream reasoning ability. Existing pre-trained language model-based knowledge graph completion methods provide strong textual semantic representations, but they usually model graph structure only as shallow auxiliary features and remain weak in distinguishing structurally similar entities and topology-near negative samples. To address this limitation, this paper proposes a Dual-Level Structural Context Collaborative Framework (DSC 2 F) for knowledge graph completion. At the instance level, the framework introduces Structural Neighborhood Context (SNC) to inject local neighborhood evidence into the language model input and Relation-Aware Attention (RAA) to condition structural aggregation on the current relation. At the batch level, it constructs topology-aware training batches with biased random walk with restart, so that in-batch negatives are locally related to positive samples and impose stronger structural discrimination pressure. Experiments on WN18RR, FB15k-237, and Wikidata5M show that DSC 2 F achieves the best mean reciprocal rank and Hits@1 on all three datasets, consistently outperforming strong embedding-based and pre-trained language model-based baselines. Ablation studies and structural configuration analyses further verify that SNC, RAA, and Batch-Level Structural Context provide complementary benefits. These results demonstrate that collaborative modeling of instance-level and batch-level structural context can effectively enhance structure-aware entity representation and improve fine-grained entity prediction.