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Eng."],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Knowledge graph has wide applications in the field of computer science. In the knowledge service environment, the information is large and explosive, and it is difficult to find knowledge of common phenomena. The urban traffic knowledge graph is a knowledge system that formally describes urban traffic concepts, entities and their interrelationships. It has great application potential in application scenarios such as user travel, route planning, and urban planning. This paper first defines the urban traffic knowledge graph and the star subgraph query of the urban traffic knowledge graph. Then, the road network data and trajectory data are collected to extract the urban traffic knowledge, and the urban traffic knowledge graph is constructed with this knowledge. Finally, a star subgraph query algorithm on the urban traffic knowledge graph is proposed. The discussion of the star subgraph query mode gives the corresponding application scenarios of our method in the urban traffic knowledge graph. Experimental results verify the performance advantages of this method.<\/jats:p>","DOI":"10.1007\/s41019-022-00198-0","type":"journal-article","created":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T19:03:16Z","timestamp":1666378996000},"page":"383-401","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["An Efficient Algorithm of Star Subgraph Queries on Urban Traffic Knowledge Graph"],"prefix":"10.1007","volume":"7","author":[{"given":"Tao","family":"Sun","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0929-5234","authenticated-orcid":false,"given":"Jianqiu","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Caiping","family":"Hu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,21]]},"reference":[{"key":"198_CR1","doi-asserted-by":"crossref","unstructured":"Ruan S, Long C, Bao J, Li C, Yu Z, Li R, Liang Y, He T, Zheng Y (2020) Learning to generate maps from trajectories. 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