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Intell. Syst. Technol."],"published-print":{"date-parts":[[2023,8,31]]},"abstract":"<jats:p>Every day, our living city produces a tremendous amount of spatial-temporal data, involved with multiple sources from the individual scale to the city scale. Undoubtedly, such massive urban data can be explored for a better city and better life, as what the urban computing community has been dedicating in recent years. Nevertheless, existing studies are still facing the challenges of data fusion for the urban data as well as the knowledge distillation for specific applications. Moreover, there is a lack of full-featured and user-friendly platforms for both researchers and developers in the urban computing scenario. Therefore, in this article, we present UrbanKG, an urban knowledge graph system to incorporate a knowledge graph with urban computing. Specifically, the system introduces a complete scheme to construct a knowledge graph for urban data fusion. Built upon the data layer, the system further develops the multiple layers of construction, storage, algorithm, operation, and applications, which achieve knowledge distillation and support various functions to the users. We perform representative use cases and demonstrate the system capability of boosting performance in various downstream applications, indicating a promising research direction for knowledge-driven urban computing.<\/jats:p>","DOI":"10.1145\/3588577","type":"journal-article","created":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T12:13:06Z","timestamp":1679400786000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":49,"title":["UrbanKG: An Urban Knowledge Graph System"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2399-2829","authenticated-orcid":false,"given":"Yu","family":"Liu","sequence":"first","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7985-6263","authenticated-orcid":false,"given":"Jingtao","family":"Ding","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1767-8024","authenticated-orcid":false,"given":"Yanjie","family":"Fu","sequence":"additional","affiliation":[{"name":"University of Central Florida"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5617-1659","authenticated-orcid":false,"given":"Yong","family":"Li","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,5,8]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3507904"},{"key":"e_1_3_2_3_2","article-title":"Learning beyond datasets: Knowledge graph augmented neural networks for natural language processing","author":"Annervaz K. 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