Construction of Urban Problem LOD using Crowdsourcing
Construction of Urban Problem LOD using Crowdsourcing
复制标题
DOI：
10.52731/ijscai.v3.i1.321
复制
发表时间：
2019-05
期刊：
影响因子：
--
通讯作者：
S. Egami;Takahiro Kawamura;Kouji Kozaki;Akihiko Ohsuga
中科院分区：
文献类型：
--
作者：
S. Egami;Takahiro Kawamura;Kouji Kozaki;Akihiko Ohsuga
日本的城市有各种各样的城市问题，如交通事故，非法停放的自行车和噪音污染。然而，使用这些数据来解决城市问题是困难的，因为这些数据不是结构化的。因此，我们的目标是构建关联数据集，以促进城市问题的解决。本文提出了一种基于Web页面和政府开放数据的城市问题因果关联数据的半自动构建方法。具体来说，我们使用自然语言处理和众包提取因果关系，以在关联数据中包含问题因果关系。然后，我们提供了一个示例查询来确认几个问题之间的关系。最后，我们讨论了我们的众包任务设计提取城市问题的因果关系。
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 between several problems. Finally, we discussed our crowdsourcing task design for extracting urban problem causality.