🤖 AI Summary
This work addresses the limitation of existing knowledge graph embedding methods, which primarily focus on binary relations and struggle to jointly model hyperedges and hyper-relations—two distinct types of n-ary facts commonly coexisting in real-world scenarios. To overcome this challenge, the authors propose HEHRGNN, a novel model that, for the first time, integrates both hyperedges and hyper-relations into a unified framework. The approach introduces a standardized HEHR representation format for n-ary facts and designs a tailored graph neural network message-passing mechanism capable of handling both structural types. This enables joint embedding learning and inductive link prediction over complex knowledge graphs. Extensive experiments on multiple real-world datasets demonstrate that HEHRGNN significantly outperforms current state-of-the-art baselines, confirming its superior capability in modeling heterogeneous n-ary facts and generalizing to unseen entities.
📝 Abstract
Knowledge Graph(KG) has gained traction as a machine-readable organization of real-world knowledge for analytics using artificial intelligence systems. Graph Neural Network(GNN), is proven to be an effective KG embedding technique that enables various downstream tasks like link prediction, node classification, and graph classification. The focus of research in both KG embedding and GNNs has been mostly oriented towards simple graphs with binary relations. However, real-world knowledge bases have a significant share of complex and n-ary facts that cannot be represented by binary edges. More specifically, real-world knowledge bases are often a mix of two types of n-ary facts - (i) that require hyperedges and (ii) that require hyper-relational edges. Though there are research efforts catering to these n-ary fact types, they are pursued independently for each type. We propose $H$yper$E$dge $H$yper-$R$elational edge $GNN$(HEHRGNN), a unified embedding model for n-ary relational KGs with both hyperedges and hyper-relational edges. The two main components of the model are i)HEHR unified fact representation format, and ii)HEHRGNN encoder, a GNN-based encoder with a novel message propagation model capable of capturing complex graph structures comprising both hyperedges and hyper-relational edges. The experimental results of HEHRGNN on link prediction tasks show its effectiveness as a unified embedding model, with inductive prediction capability, for link prediction across real-world datasets having different types of n-ary facts. The model also shows improved link prediction performance over baseline models for hyperedge and hyper-relational datasets.