Knowledge Graph Reasoning Based on Graph Neural Networks

Abstract

This paper explores a novel approach to knowledge graph reasoning utilizing Graph Neural Networks (GNNs). Traditional knowledge graph reasoning methods often struggle with scalability and accurately capturing complex relationships within vast knowledge bases. This work proposes a framework that represents knowledge graphs as graph structures and leverages the power of GNNs for both node classification and relation prediction. The core claim is that GNNs can effectively learn and propagate knowledge across a knowledge graph, ultimately leading to improved reasoning performance. The proposed mechanism utilizes message-passing techniques within GNNs to aggregate information from a node's neighbors, allowing the model to understand the context surrounding each entity and predict relevant relationships. We demonstrate that this approach enhances both the efficiency and accuracy of knowledge graph inference, addressing key limitations of existing methods. The primary contribution lies in the application of GNNs to this domain, offering a scalable and robust solution for knowledge graph reasoning. ---