Local-Global Collaborative Relational Representation for Understanding Knowledge Graphs

Knowledge graphs (KGs) typically have distinct entity and relation vocabularies, there is usually no overlap between the vocabularies of different KGs. Consequently, most existing studies develop independent reasoning models for different KGs. However, such models generally lack generalization capability in reasoning. This paper proposes a novel model Local-Global Collaborative Relational Representation for KG Reasoning(LGRR) to achieve universal reasoning by learning relational invariance in KGs. Specifically, we introduce a local-global relational graph embedding method, which employs a graph neural network with an attention mechanism to perform local message passing. Then, global attention is applied to information propagation to overcome the limitations of traditional local message passing and enhance capturing more comprehensive graph structural information. Finally, in the local message passing phase, we propose a relation-aware dynamic attention mechanism and a relation aggregation strategy. By integrating relation-type semantics with local subgraph structural features, our method dynamically generates attention coefficients among nodes, thereby enhancing the model reasoning capability. The results demonstrate that the zero-shot (0-shot) inference capability of a single pre-trained LGRR model is comparable or superior to models trained on specific KGs across most unseen KGs.