{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T09:24:09Z","timestamp":1772011449615,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2018,7,7]],"date-time":"2018-07-07T00:00:00Z","timestamp":1530921600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"publisher","award":["61473260\/61673338"],"award-info":[{"award-number":["61473260\/61673338"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"key S&amp;T Special projects","award":["2015ZX03003012-004"],"award-info":[{"award-number":["2015ZX03003012-004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Urban computing at present often relies on a large number of manually extracted features. This may require a considerable amount of feature engineering, and the procedure may miss certain hidden features and relationships among data items. In this paper, we propose a method to use structured prior knowledge in the form of knowledge graphs to improve the precision and interpretability in applications such as optimal store placement and traffic accident inference. Specifically, we integrate sub-graph feature extraction, sub-knowledge graph gated neural networks, and kernel-based knowledge graph convolutional neural networks as ways of incorporating large urban knowledge graphs into a fully end-to-end learning system. Experiments using data from several large cities showed that our method outperforms the baseline methods.<\/jats:p>","DOI":"10.3390\/ijgi7070264","type":"journal-article","created":{"date-parts":[[2018,7,9]],"date-time":"2018-07-09T11:18:53Z","timestamp":1531135133000},"page":"264","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Structured Knowledge Base as Prior Knowledge to Improve Urban Data Analysis"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1970-0678","authenticated-orcid":false,"given":"Ningyu","family":"Zhang","sequence":"first","affiliation":[{"name":"Artificial Intelligence and Future Network Technology Research Institute, Zhejiang Lab, Hangzhou 311121, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shumin","family":"Deng","sequence":"additional","affiliation":[{"name":"Colledge of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China"},{"name":"Alibaba-Zhejiang University Joint Institue of Frontier Technologies, AIBABA, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huajun","family":"Chen","sequence":"additional","affiliation":[{"name":"Colledge of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China"},{"name":"Alibaba-Zhejiang University Joint Institue of Frontier Technologies, AIBABA, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Chen","sequence":"additional","affiliation":[{"name":"Colledge of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China"},{"name":"Alibaba-Zhejiang University Joint Institue of Frontier Technologies, AIBABA, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaoyan","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Oxford University, Oxford OX1 3QR, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoqian","family":"Li","sequence":"additional","affiliation":[{"name":"Artificial Intelligence and Future Network Technology Research Institute, Zhejiang Lab, Hangzhou 311121, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Artificial Intelligence and Future Network Technology Research Institute, Zhejiang Lab, Hangzhou 311121, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,7,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gomez-Perez, J.M., Pan, J.Z., Vetere, G., and Wu, H. (2017). Enterprise KnowledgeGraph: An Introduction. Exploiting Linked Data and Knowledge Graphs in Large Organisations, Springer.","DOI":"10.1007\/978-3-319-45654-6_1"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Pan, J.Z., Vetere, G., Gomez-Perez, J.M., and Wu, H. (2017). Exploiting Linked Data and Knowledge Graphs in Large Organisations, Springer.","DOI":"10.1007\/978-3-319-45654-6"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chen, X., Shrivastava, A., and Gupta, A. (2013, January 1\u20138). Neil: Extracting visual knowledge from web data. Proceedings of the IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.178"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Mitchell, T., and Fredkin, E. (2014, January 27\u201330). Never ending language learning. Proceedings of the 2014 IEEE International Conference on Big Data, Washington, DC, USA.","DOI":"10.1109\/BigData.2014.7004203"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1145\/219717.219748","article-title":"WordNet: A lexical database for English","volume":"38","author":"Miller","year":"1995","journal-title":"Commun. ACM"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1145\/2629489","article-title":"Wikidata: A free collaborative knowledgebase","volume":"57","year":"2014","journal-title":"Commun. ACM"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1023\/B:BTTJ.0000047600.45421.6d","article-title":"ConceptNet\u2014A practical commonsense reasoning tool-kit","volume":"22","author":"Liu","year":"2004","journal-title":"BT Technol. J."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lao, N., Minkov, E., and Cohen, W.W. (2015). Learning Relational Features with Backward Random Walks, Atlantic Container Line.","DOI":"10.3115\/v1\/P15-1065"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Gardner, M., and Mitchell, T.M. (2015, January 17\u201321). Efficient and Expressive Knowledge Base Completion Using Subgraph Feature Extraction. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal.","DOI":"10.18653\/v1\/D15-1173"},{"key":"ref_10","unstructured":"Li, Y., Tarlow, D., Brockschmidt, M., and Zemel, R. (arXiv, 2015). Gated graph sequence neural networks, arXiv."},{"key":"ref_11","unstructured":"Niepert, M., Ahmed, M., and Kutzkov, K. (2017, January 4\u20138). Learning convolutional neural networks for graphs. Proceedings of the 33rd Annual International Conference on Machine Learning, Orlando, FL, USA."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1109\/JIOT.2017.2748987","article-title":"Sensor fusion for public space utilization monitoring in a smart city","volume":"5","author":"Lau","year":"2018","journal-title":"IEEE Int. Things J."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Lau, B.P.L., Yuen, C., Tun\u00e7er, B., and Wilhelm, E. (arXiv, 2018). Understand Urban Human Mobility through Crowdsensed Data, arXiv.","DOI":"10.1109\/MCOM.2018.1700569"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, N., Chen, H., Chen, X., and Chen, J. (2016). ELM Meets Urban Computing: Ensemble Urban Data for Smart City Application, Springer.","DOI":"10.1155\/2016\/4970246"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, N., Chen, H., Chen, J., and Chen, X. (2016). Social media meets big urban data: A case study of urban waterlogging analysis. Comput. Intell. Neurosci., 2016.","DOI":"10.1155\/2016\/3264587"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, N., Zheng, G., Chen, H., Chen, X., and Chen, J. (2014, January 8\u201312). Monitoring urban waterlogging disaster using social sensors. Proceedings of the Chinese Semantic Web and Web Science Conference, Wuhan, China.","DOI":"10.1007\/978-3-662-45495-4_20"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, N., Chen, H., Chen, X., and Chen, J. (2016). Forecasting public transit use by crowdsensing and semantic trajectory mining: Case studies. ISPRS Int. J. Geo-Inf., 5.","DOI":"10.3390\/ijgi5100180"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, N., Chen, H., Chen, X., and Chen, J. (2016). Semantic framework of internet of things for smart cities: Case studies. Sensors, 16.","DOI":"10.3390\/s16091501"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1108\/09590550010337391","article-title":"The art and science of retail location decisions","volume":"28","author":"Hernandez","year":"2000","journal-title":"Int. J. Retail Distrib. Manag."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/s10037-006-0010-3","article-title":"Analysis of location of large-area shopping centres. A probabilistic Gravity Model for the Halle\u2013Leipzig area","volume":"27","author":"Kubis","year":"2007","journal-title":"Jahrbuch Regionalwissenschaft"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Xiao, X., Yao, B., and Li, F. (2011, January 11\u201316). Optimal location queries in road network databases. Proceedings of the 2011 IEEE 27th International Conference on Data Engineering (ICDE), Hannover, Germany.","DOI":"10.1109\/ICDE.2011.5767845"},{"key":"ref_22","first-page":"14","article-title":"Site for store buys","volume":"5","author":"Rogers","year":"1997","journal-title":"New Perspect."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, Y., Zheng, Y., Ji, S., Wang, W., and Gong, Z. (2015, January 3\u20136). Location selection for ambulance stations: A data-driven approach. Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, Seattle, WA, USA.","DOI":"10.1145\/2820783.2820876"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Graells-Garrido, E., Peredo, O., and Garc\u00eda, J. (2016). Sensing urban patterns with antenna mappings: The case of Santiago, Chile. Sensors, 16.","DOI":"10.3390\/s16071098"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Huang, T., Bergman, D., and Gopal, R. (arXiv, 2018). Predictive and Prescriptive Analytics for Location Selection of Add-on Retail Products, arXiv.","DOI":"10.1111\/poms.13018"},{"key":"ref_26","unstructured":"Ching, W., Chu, A., Hin, M., and Chan, E. (2017, January 3\u20136). A Retail Gravity Model for Selecting the Optimal Store Location. Proceedings of the 2017 World Transport Convention, Beijing, China."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chen, T.Y., Chen, L.C., and Chen, Y.M. (2017). Mining Location-Based Service Data for Feature Construction in Retail Store Recommendation, Springer.","DOI":"10.1007\/978-3-319-62701-4_6"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Karamshuk, D., Noulas, A., Scellato, S., Nicosia, V., and Mascolo, C. (2013, January 11\u201314). Geo-spotting: mining online location-based services for optimal retail store placement. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, USA.","DOI":"10.1145\/2487575.2487616"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.jtrangeo.2013.05.009","article-title":"Detecting traffic accident clusters with network kernel density estimation and local spatial statistics: An integrated approach","volume":"31","author":"Xie","year":"2013","journal-title":"J. Trans. Geogr."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.aap.2013.03.003","article-title":"Identification of hazardous road locations of traffic accidents by means of kernel density estimation and cluster significance evaluation","volume":"55","year":"2013","journal-title":"Accid. Anal. Prev."},{"key":"ref_31","unstructured":"Malisiewicz, T., and Efros, A. (2009, January 7\u201310). Beyond Categories: The Visual Memex Model for Reasoning about Object Relationships. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Fathi, A., and Fei-Fei, L. (2014). Reasoning about Object Affordances in a Knowledge Base Representation, Springer.","DOI":"10.1007\/978-3-319-10605-2_27"},{"key":"ref_33","unstructured":"(2018, July 05). Road Network Segementation. Available online: https:\/\/github.com\/zxlzr\/Segment-Maps."},{"key":"ref_34","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet classification with deep convolutional neural networks. Proceedings of the 25th International Conference on Neural Information Processing Systems, Lake Tahoe, Nevada."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000006","article-title":"Learning deep architectures for AI","volume":"2","author":"Bengio","year":"2009","journal-title":"Found. Trends Mach. Learn."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_37","unstructured":"(2018, July 05). Soda Data of Shanghai, Available online: http:\/\/soda.datashanghai.gov.cn\/."},{"key":"ref_38","unstructured":"(2018, July 05). Wikidata. Available online: http:\/\/www.wikidata.org."},{"key":"ref_39","unstructured":"(2018, July 05). ConceptNet5. Available online: http:\/\/github.com\/commonsense\/conceptnet5."},{"key":"ref_40","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (arXiv, 2016). TensorFlow: A System for Large-Scale Machine Learning, arXiv."},{"key":"ref_41","unstructured":"Chollet, F. (2018, July 05). Keras. Available online: https:\/\/github.com\/keras-team\/keras."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1145\/582415.582418","article-title":"Cumulated gain-based evaluation of IR techniques","volume":"20","year":"2002","journal-title":"ACM Trans. Inf. Syst."},{"key":"ref_43","unstructured":"Li, J., and Deshpande, A. (July, January 29). Consensus answers for queries over probabilistic databases. Proceedings of the Twenty-Eighth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database System, Providence, RI, USA."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/7\/7\/264\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:11:39Z","timestamp":1760195499000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/7\/7\/264"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,7]]},"references-count":43,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2018,7]]}},"alternative-id":["ijgi7070264"],"URL":"https:\/\/doi.org\/10.3390\/ijgi7070264","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,7,7]]}}}