Role-Aware and Type-Aware Hyper-Relational Knowledge Graph Completion

Knowledge graphs are foundational to structured knowledge representation and drive the evolution of artificial intelligence from data-driven to knowledge-enhanced paradigms. Hyper-relational knowledge graphs employ hyperedge structures to overcome the expressive limitations of conventional triple-based schemes in modeling complex multi-entity facts with intricate dependency constraints. However, prevailing completion methods prioritize global structural patterns while neglecting entity role dependencies and type-level semantic priors, rendering them vulnerable to data sparsity and semantic noise. This study proposes RATA, a hyper-relational knowledge graph completion model that integrates role-aware and type-aware mechanisms to mitigate these drawbacks. The model uniformly encodes hyper-relational facts through hyperedge representations and captures global semantics using a bidirectional node–hyperedge attention module. It leverages role-aware sequence modeling to explicitly represent entity semantic roles and incorporates entity type priors to construct hierarchical semantics for aligning instance- and type-level information, thereby fusing structural, role-semantic, and type-prior knowledge. Evaluations on JF17K, WD50K, and WikiPeople show that RATA achieves competitive performance on entity and relation completion tasks. Ablation experiments validate the effectiveness of its hyperedge global encoding, role-aware modeling, and type-aware representation learning modules, indicating that RATA provides viable technical support for complex hyper-relational knowledge graph completion.