A Representation Learning Framework of Ship Knowledge Graph for Military Target Discovery
As China's ship fleet formation expands,the most influential data among the many ship data has become an important target that needs attention.To address the problem of difficulty in the timely analysis of ship data,are presentation learning algorithm,the Relational Graph Transformer Network(RGTN)is introduced into ship knowledge graph analysis field for the first time.Based on the structure and semantic characteristics of the ship knowledge graph,a node importance evaluation method based on representation learning is studied to process the ship knowledge graph and the importance of nodes in the ship knowledge graph is predicted.This node importance evaluation algorithm performs better than that of the previous node importance evaluation algorithm in the ship knowledge graph and is more suitable for ship knowledge graph analysis field.
万方数据
维普
