Adaptive Graph Neural Network with Incremental Learning Mechanism for Knowledge Graph Reasoning

Abstract

Knowledge graphs are extensively utilized in diverse fields such as search engines, recommendation systems, and dialogue systems, and knowledge graph reasoning plays an important role in the aforementioned domains. Graph neural networks demonstrate the capability to effectively capture and process the graph structure inherent in knowledge graphs, leveraging the relationships between nodes and edges to enable efficient reasoning. Current research on graph neural networks relies on predefined propagation paths. The models based on predefined propagation paths overlook the correlation between entities and query relations, limiting adaptability and scalability. In this paper, we propose an adaptive graph neural network with an incremental learning mechanism to search the adaptive propagation path in order to retain promising targets. In detail, the incremental learning mechanism is able to filter out unrelated entities in the propagation path by incorporating the node sampling technique to increase the learning efficiency of the model. In addition, the incremental learning mechanism can select semantically related entities, which promotes the capture of meaningful connections among different nodes in the knowledge graph. At the same time, we apply the parameter initialization module to accelerate the convergence and improve the robustness of the model. Experimental results on benchmark datasets demonstrate that the adaptive graph neural network with the incremental learning mechanism has excellent semantic awareness ability, which surpasses several excellent knowledge graph reasoning models.