A construction method of urban road risky vehicles based on dynamic knowledge graph

Abstract

The growth of the Internet of Things makes it possible to share information on risky vehicles openly and freely. How to create dynamic knowledge graphs of continually changing risky vehicles has emerged as a crucial technology for identifying risky vehicles, as well as a research hotspot in both artificial intelligence and field knowledge graphs. The node information of the risky vehicle knowledge graph is not rich, and the graph structure plays a major role in its dynamic changes. The paper presents a fusion algorithm based on relational graph convolutional network (R-GCN) and Long Short-Term Memory (LSTM) to build the dynamic knowledge graph of risky vehicles and conducts a comparative experiment on the link prediction task. The results showed that the fusion algorithm based on R-GCN and LSTM had better performance than the other methods such as GCN, DynGEM, ROLAND, and RE-GCN, with the MAP value of 0.2746 and the MRR value of 0.1075. To further verify the proposed algorithm, classification experiments are carried out on the risky vehicle dataset. Accuracy, precision, recall, and F-values were used as heat-tolerance evaluation indexes in classification experiments, the values were 0.667, 0.034, 0.422, and 0.52 respectively.

Keywords

• dynamic knowledge graph
• risky vehicles
• r-gcn
• lstm
• fusion algorithm