SNAP-KG: Streaming Node Assignment via Projection for Knowledge Graph Entity Integration

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.

Relation-Aware Multi-View Representation Learning for Cross-KG Relation Prediction

Cross-KG relation prediction aims to predict possible relations between entities from different knowledge graphs. This task is difficult because different graphs often have different structures, use different relation types, and share only a small number of entities. Under this setting, it is not easy to pass useful information across graphs. In this paper, we propose a relation-aware multi-view representation learning framework for cross-KG relation prediction. We first build a unified graph by connecting two knowledge graphs through overlapping entities. Based on this graph, we learn entity representations from two complementary views: a structural view that captures graph topology, and a relation-aware view that uses relation information to guide message passing through a gating mechanism. The two views are then fused to obtain final entity embeddings, which are used to rank candidate cross-KG relations. Experiments on DFB-1, WK3L-E, and MEDICINE show that our method consistently outperforms competitive baselines, including the dedicated cross-KG model CrossKG. In particular, compared with CrossKG, our method reduces MR by 65.1%, 5.7%, and 33.7% on the three datasets, respectively. Ablation and overlap-ratio experiments further show that the two views work well together, and that relation-aware propagation is especially helpful when the overlap between graphs is low.

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

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.

LLMs as Parametric Knowledge Sources for Knowledge Graph Completion

This framework performs LLM knowledge elicitation to extract factual knowledge from the model’s internal representations and transforms sentence-level representations into entity-level representations and aligns them within a unified space.

Multi-relational knowledge graph completion with joint embedding of entities and relations

MolBioKG: Grounding Out-of-Graph Molecules in Biomedical Knowledge Graphs via Multi-Resolution Structural Anchoring

This work introduces MolBioKG, a two-layer system that grounds unseen molecules in biomedical evidence via multi-resolution structural anchoring and outperforms strong baselines across in-graph link recovery, complex multi-hop reasoning, and out-of-graph generalization.