Construction of Linked Urban Problem Data with Causal Relations Using Crowdsourcing

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 managed. Hence, we aim to construct the Linked Data infrastructure 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. As a result, Linked Data with causal relations of noise pollution, illegally parked bicycles, and traffic accidents was constructed.