Construction of Urban Problem LOD using Crowdsourcing

  • Shusaku Egami The University of Electro-Communications
  • Takahiro Kawamura Japan Science and Technology Agency
  • Kouji Kozaki Osaka University
  • Akihiko Ohsuga The University of Electro-Communications
Keywords: Linked Open Data, Crowdsourcing, Urban problem, Causal Relation Extraction


Municipalities in Japan have various urban problems such as traffic accidents, illegally parked bicycles, and noise pollution. However, using these data to solve urban problems is difficult, as these data are not structurally constructed. Hence, we aim to construct the Linked Data set that will facilitate the solving of urban problems. In this paper, we propose a method for semi-automatic construction of Linked Data with the causality of urban problems, based on Web pages and open government data. Specifically, we extracted causal relations using natural language processing and crowdsourcing to include problem causality in the Linked Data. Then, we provided an example query to confirm the relationships be-tween several problems. Finally, we discussed our crowdsourcing task design for extracting urban problem causality.


P. Szek ely, C. A. Knoblock, J. Slepicka, et a, ”Building and Using a Knowledge Graph to Combat Human Trafficking,” In: Proceedings of the 14th International Semantic Web Conference (ISWC), 2015, pp.205-221

S. Egami, T. Kawamura, Y. Sei, Y. Tahara, A. Ohsuga, ”Desiging and Publishing Illegally Parked Bicycle LOD” InternationalJournal of Smart Computing and Artificial Intelligence, Vol.1, No.2, 2017, pp.77-93

S. Egami, T. Kawamura, A. Ohsuga, ”Building UrbanLOD for Solving Illegally Parked Bicycles in Tokyo,” In: Proceedings of the 15th InternationalSemantic Web Conference (ISWC), 2016, pp.291-307

S. Shiramatsu, T. Tossavainen, T. Ozono, T. Shintani, ”Towards Continuous Collaboration on Civic Tech Projects: Use Cases of a Goal Sharing System Based on Linked Open Data,” In: Proceeding of the 7th IFIP International Conference on Electronic Participation (ePart), 2015, pp.81-92

H. Santos, V. Dantas, V. Furtado, P. Pinheiro, D, D. L. McGuinness, ”From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of Dashboards,” In: Proceeding of the 14th Extended Semantic Web Conference (ESWC), 2017, pp.94-108

S. F. Pileggi, J. Hunter, ”An ontological approach to dynamic fine-grained Urban Indicators,” Procedia ComputerScience, Vol. 108, 2017, pp.2059-2068

K. Hoffner, M. Martin, J. Lehmann, ”LinkedSpending: OpenSpending becomes Linked ¨ Open Data,” Semantic Web Journal, Vol.7, No.1, 2016, pp.95-104

G. Demartini, D. E. Difallah, P. Cudre-Mauroux, ”Large-scale linked data integration ´ using probabilistic reasoning and crowdsourcing,” The International Journal on Very Large Data Bases, Vol. 22, No. 5, 2013, pp.665-687

I. Celino, S. Contessa, M. Corubolo, D. Dell ’Aglio, E. D. Valle, S. Fumeo, T. Kruger, ¨ ”Linking Smart Cities Datasets with HumanComputation- The Case of UrbanMatch,” In: Proceedings of the 11th International Semantic Web Conference (ISWC), 2011, pp.34-49

L. V. Ahn, ”Games with a purpose,” IEEE Computer, Vol. 39, No. 6, 2006, pp. 92-94

T. Kudo, Y. Matsumoto, ”Japanese Dependency Analyisis using Cascaded Chunking,” In: Proceedings of the 6th Conference on Natural Language Learning, Vol.20, 2002, pp.1-7

W. Winkler, ”The state record linkage and current research problems,” Technical report, Statistics of Income Division, Internal Revenue Service Publication,1999

T. M. Nguyen, T. Kawamura, Y. Tahara, A. Ohsuga, ”Self-Supervised Capturing of Users’ Activities from Weblogs,” International Journal of Intelligent Information and Database Systems, Vol. 6, No. 1, 2012, pp.61-76

I. Augenstein, S. Pado, S. Rudolph, ”LODifier: Generating Linked Data from Un- ´ structuredText,” In: Proceedings of the Extended Semantic Web Conference (ESWC), 2012, pp.210-224

D. Milne, I. H. Witten, ”Learning to Link with Wikipedia,” In: Proceedings of the 17th ACM conference on Informationand knowledge management (CIKM), 2008, pp. 509-518

H. Sakaji, S. Sekine, S. Masuyama, ”Extracting causal knowledge using clue phrases and syntactic patterns,” In: Proceedings of the International Conference on Practical Aspects of Knowledge Management (PAKDD), 2008, pp.111-122

H. Ishii, Q. Ma, M. Yoshikawa, ”Causal Network Construction to Support Understanding of News,” In: Proceedings of the 43rd Hawaii International Conference of System Sciences (HICSS), 2010, pp.1-10

D. Kontokostas, P. Westphal, S. Auer, S. Hellmann, J. Lehmann, R. Cornelissen, A. Zaveri, ”Test-driven Evaluation of Linked Data Quality,” In: Proceedings of the 23rd InternationalConference on World Wide Web (WWW), 2014, pp.747-758

J. L. Fleiss, J. Cohen, ”The Equivalence of Weighted Kappa and the Intraclass Correlation Coefficient as Measures of Reliability,” Educational and Psychological Measurement, Vol.33, No.3, 1973, pp.613-619

A. J. Viera, J. M. Garrett, ”Understanding interobserver agreement: the kappa statistic,” Fam Med, Vol.37, No.5, 2005, pp.360-363

M. Ashikawa, T. Kawamura, A. Ohsuga, ”Crowdsourcing worker development based on probabilistic task network,” Proceedings of the International Conference on Web Intelligence (WI), 2017, pp.855-862