Abstract
There are various urban problems, such as suburban crime, dead shopping street, and littering. However, various factors are socially intertwined; thus, structural management of the related data is required for visualizing and solving such problems. Moreover, in order to implement the action plans, local governments first need to grasp the cost-effectiveness. Therefore, this paper aims to construct Linked Open Data (LOD) that include causal relations of urban problems and the related cost information in the budget. We first designed a data schema that represents the urban problems’ causality and extended the schema to include budget information based on QB4OLAP. Next, we semi-automatically enriched instances according to the schema using natural language processing and crowdsourcing. Finally, as use cases of the resulting LOD, we provided example queries to extract the relationships between several problems and the particular cost information. We found several causes that lead to the vicious circle of urban problems and for the solutions of those problems, we suggest to a local government which actions should be addressed.
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Acknowledgments
This work was supported by JSPS KAKENHI Grant Numbers 16K12411, 16K00419, 16K12533, 17H04705.
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Egami, S., Kawamura, T., Kozaki, K., Ohsuga, A. (2017). Linked Urban Open Data Including Social Problems’ Causality and Their Costs. In: Wang, Z., Turhan, AY., Wang, K., Zhang, X. (eds) Semantic Technology. JIST 2017. Lecture Notes in Computer Science(), vol 10675. Springer, Cham. https://doi.org/10.1007/978-3-319-70682-5_23
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