Hyper-Relational Knowledge Graph Representation Learning Based on Multi-Granularity Semantic Aware Message Passing

Real-world networks containing hyper-relational facts can be represented by Hyper-relational Knowledge Graphs (HKGs), which contain rich semantics compared to hypergraphs. In an HKG, multiple entities with a shared property playing different roles form a hyperedge. We propose an HKG representation learning method based on Multi-granularity Semantic aware Message Passing (HyperSMP), preserving both semantic and structural information. Specifically, we design a fine-grained aggregation layer in HyperSMP to aggregate different roles of entities in hyperedge embeddings using an entity-level attention mechanism. Based on hyperedge embeddings, we propose a granularity-aligned propagation layer that recursively propagates information from hyperedges to entities and properties, capturing message passing paths in and out of hyperedges, respectively. As a result, multi-grained semantics are learned through unification at the hyperedge level to adapt to the message passing mechanism, and the high-order structure is captured through entity information exchange channeled via hyperedge embeddings. Further, HyperSMP discovers multi-hop associations among entities by stacking the above layers.