HD iff: Confidence-Guided Denoising Diffusion for Robust Hyper-relational Link Prediction (2025)

Abstract: Although Hyper-relational Knowledge Graphs (HKGs) can model complex facts better than traditional KGs, the Hyper-relational Knowledge Graph Completion (HKGC) is more sensitive to inherent noise, particularly struggling with two prevalent HKG-specific noise types: Intra-fact Inconsistency and Cross-fact Association Noise.To address these challenges, we propose **HDiff**, a novel conditional denoising diffusion framework for robust HKGC that learns to reverse structured noise corruption. HDiff integrates a **Consistency-Enhanced Global Encoder (CGE)** using contrastive learning to enforce intra-fact consistency and a **Context-Guided Denoiser (CGD)** performing iterative refinement. The CGD features dual conditioning leveraging CGE’s global context and local confidence estimates, effectively combatting both noise types. Extensive experiments demonstrate that HDiff substantially outperforms state-of-the-art HKGC methods, highlighting its effectiveness and significant robustness, particularly under noisy conditions.

## introduction hyper-relational knowledge graphs (hkgs) @xcite extend traditional kgs @xcite by augmenting core facts (s, r, o) with qualifier sets @xmath0, v i )} @xcite . this richer structure captures complex factual statements with crucial context, vital for applications like detailed question answering and biomedical discovery @xcite . hyper-relational knowledge graph completion (hkgc)-predicting missing elements in these hyper-relational facts (h-facts)-is thus essential for enriching these knowledge bases. early hkgc approaches often adapted traditional kg embedding techniques, utilizing compositional scoring functions to integrate qualifier * corresponding author. information @xcite . more recently, methods employing graph neural networks (gnns) have gained prominence, leveraging the inherent graph or hypergraph structure of hkgs @xcite . these gnn-based models typically feature specialized message-passing schemes or attention mechanisms designed to effectively aggregate information from both core triple components and associated qualifiers @xcite . by capturing intricate structural dependencies and learning expressive representations, these methods have significantly advanced the state of the art in hkgc. nevertheless, their success largely hinges on the assumption of relatively clean data, often neglecting the pervasive issue of noise commonly found in real-world knowledge graphs. crucially, the complex structure of h-facts, particularly the interplay involving qualifiers, introduces distinct noise patterns that pose unique challenges beyond those encountered in simpler triplebased kgs (see figure 1 ). we identify two prevalent and particularly detrimental hkg-specific noise types arising from these qualifier interactions: these specific noise patterns severely undermine the effectiveness of existing hkgc methods @xcite . methods relying on aggregation (like gnns) may propagate inconsistencies, while those using scoring functions might assign high confidence to internally contradictory facts. standard noise mitigation techniques developed for traditional kgs @xcite often prove insufficient as they are typically not designed to handle inconsistencies stemming from the complex interplay of qualifiers within and across h-facts. to address these challenges, we propose hdiff, a novel conditional denoising diffusion framework @xcite for robust hkgc. hdiff adopts a generative approach, learning to reverse a structured noise corruption process. it integrates two synergistic components: (1) a consistency-enhanced global encoder (cge) employs contrastive learning to enforce intra-fact semantic coherence, directly counteracting intra-fact inconsistency noise. (2) a context-guided denoiser (cgd) performs iterative refinement conditioned on both global context (from cge) and local component confidence estimates, robustly mitigating both noise types by balancing global structure and local uncertainty. therefore, our main contributions are threefold: ## methodology we propose hdiff, a novel conditional denoising diffusion framework specifically designed for robust hyper-relational knowledge graph completion (hkgc). hdiff addresses hkg noise via two core components: a consistency-enhanced global encoder (cge) generating noise-resilient, semantically consistent initial embeddings, and a context-guided denoiser (cgd) for iterative refinement via conditional diffusion, guided by local and global context for accurate, robust completion. figure 2 provides a schematic overview of the proposed hdiff architecture. ## conclusion intra-fact inconsistency and cross-fact association noise for robust hyperrelational knowledge graph completion (hkgc). by integrating a consistency-enhanced global encoder (cge) with contrastive learning and a context-guided denoiser (cgd) employing unique dual conditioning, hdiff facilitates robust reconstruction of clean h-facts from noisy inputs. experiments demonstrated state-of-the-art performance and superior noise robustness, with ablations validating core component contributions. hdiff represents a notable advancement towards reliable hkgc in complex and noisy environments. future work can explore efficiency improvements and broader hyper-relational reasoning tasks.