Yangyang Wei
ABSTRACT
Knowledge graph can clearly express the relationship between entities and entities, and its integration can more intuitively and clearly allow us to understand and use knowledge units. Since Google first proposed knowledge graphs in 2012, knowledge graphs have attracted more attention in related fields such as industry. As knowledge graphs have attracted more and more attention in recent years, they have developed rapidly in various fields, especially in the medical field and e-commerce fields. However, the application of knowledge graphs to the transportation field is not very extensive. This article is based on the processing of relevant experience articles in the transportation industry to construct a traffic knowledge graph. The construction process is roughly divided into: a. Obtaining traffic knowledge. b. Recognizing traffic entities. c. Obtaining the relationship between traffic entities and entities. d. Traffic knowledge graphing Construct. This article mainly through the research and processing of data in the field of transportation. By building a traffic knowledge graph and using NEO4J graph database to build a database of information related to the traffic field, to integrate the knowledge of the traffic field, users can understand traffic knowledge more intuitively and quickly, and understand the traffic knowledge unit and its related knowledge more clearly unit.
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Index Terms
- Optimization and Research of Urban Traffic Data Based on Knowledge Graph
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