Design And Implementation Of Neural Network-Based Knowledge Graph InferencePlatform Realization

In recent years,the field of knowledge graphs has become increasingly important due to the explosion of data in various fields.Knowledge graphs are semantic networks that reveal relationships between entities and allow for a formal description of real-world things and their interrelationships.Knowledge graphs provide a structured representation of knowledge that can be easily queried and reasoned about.Graph Neural Network(GNN,Graph Nerual Network)has emerged as a promising approach for reasoning over knowledge graphs.GNNs can effectively predict complex relationships between entities and attributes in knowledge graphs by learning node embeddings that capture graph structure and entity attributes for reasoning over knowledge graphs.However,the scalability of graph neural networks remains a major challenge,with computational and memory limitations becoming a bottleneck as the size of the knowledge graph grows.Existing approaches to solve this problem have their limitations,such as working based on the idea of node sampling,which affects the accuracy of the model,and there are some hardware acceleration techniques such as GPUs and TPUs to solve this problem,but they require the use of specialized hardware.In order to solve the scalability problem of graph neural networks,this paper firstly proposes a scalable framework for distributed graph neural networks based on the idea of bi-modal communication optimization design of distributed graph computing engine Gemini combined with the idea of historical embedding information approximation of graph scalable framework GAS(GNNAutoScale).The proposed framework provides a practical solution to the scalability problem of GNNs by distributing the workload over multiple computational nodes to achieve parallelization of the model.This approach allows the use of larger knowledge graphs in inference scenarios without compromising accuracy or requiring specialized hardware.Second,this paper applies the framework to knowledge inference scenarios,implements R-GCNs using the framework,and verifies the performance of graph neural network models implemented using the framework.Finally,this paper explores the application of the framework in the cloud-native Kubeflow architecture,verifies the availability and horizontal scalability of the framework in flexible clusters,introduces how the framework is used in the Kubeflow architecture,and conducts experiments to verify its performance under different cluster sizes and loads.