Construction of Urban Problem LOD using Crowdsourcing

Shusaku Egami, Takahiro Kawamura, Kouji Kozaki, Akihiko Ohsuga
2019 International journal of smart computing and artificial intelligence  
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
more » ... a. 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. 1 72 links, and also help to consider the countermeasures that are proposed by the municipalities. In this paper, we designed a data schema representing urban problems causality. Then, we propose a method for semi-automatically extracting causalities of urban problems using natural language processing (NLP) and crowdsourcing. Finally, as a use case of the resulting LOD, we provide an example query to confirm the relationships between several problems. Our contributions are as follow: Web Search API 6 . We separately collected 50 HTML files and 50 PDF files for each keyword sets (different com-binations of the first and second keywords). We collected HTMLs and PDFs separately to Copyright © by IIAI. Unauthorized reproduction of this article is prohibited. 9
doi:10.52731/ijscai.v3.i1.321 fatcat:zqxshomlizgmfcio7gno5tgx4q