Automatic construction and optimization method of enterprise data asset knowledge graph based on graph attention network

Abstract

Traditional knowledge graph construction methods often rely on a large amount of human intervention and specialized knowledge, which seems inefficient and error-prone when dealing with massive, multi-source, and heterogeneous data assets of enterprises. To address this issue, a Multi Neighborhood Awareness Network (MNAN) model was proposed, which combines a multi language pre training model based on Bidirectional Encoder Representations from Transformers (BERT) and a Graph Convolutional Neural Network (GCN). By introducing an improved attention mechanism to deeply explore the neighborhood characteristics of entities, the accuracy and efficiency of entity matching are enhanced. In addition, to improve the integrity of the knowledge graph, a dynamic graph attention network (RDGAT) model based on graph attention network fusion relationship features is proposed. Through dynamic attention mechanism and relationship fusion strategy, deep learning of relationship features is carried out to optimize the completion performance of the knowledge graph spectrum. The performance test results show that the Hits@1 metric of the multi-neighborhood perceptual network model is close to 90% as the proportion of aligned entity seeds increases to 50%; the average inverse rank of the dynamic graph attention network on the test dataset improves by 6.3%. The experimental results show that the research-proposed knowledge graph fusion and complementation model exhibits significant effectiveness in automatically identifying and integrating large-scale, multi-source heterogeneous data at the enterprise level, and can significantly improve the automation level and accuracy of knowledge graph construction. The research method has achieved significant results in the data asset knowledge graph of petroleum enterprises and other fields, providing strong support for applications such as intelligent search, recommendation systems, and semantic analysis.