期刊文献+
共找到1篇文章
< 1 >
每页显示 20 50 100
Detecting Vicious Cycles in Urban Problem Knowledge Graph using Inference Rules
1
作者 Shusaku Egami Takahiro Kawamura +1 位作者 Kouji Kozaki Akihiko Ohsuga 《Data Intelligence》 EI 2022年第1期88-111,共24页
Urban areas have many problems,including homelessness,graffiti,and littering.These problems are influenced by various factors and are linked to each other;thus,an understanding of the problem structure is required in ... Urban areas have many problems,including homelessness,graffiti,and littering.These problems are influenced by various factors and are linked to each other;thus,an understanding of the problem structure is required in order to detect and solve the root problems that generate vicious cycles.Moreover,before implementing action plans to solve these problems,local governments need to estimate cost-effectiveness when the plans are carried out.Therefore,this paper proposed constructing an urban problem knowledge graph that would include urban problems’causality and the related cost information in budget sheets.In addition,this paper proposed a method for detecting vicious cycles of urban problems using SPARQL queries with inference rules from the knowledge graph.Finally,several root problems that led to vicious cycles were detected.Urban-problem experts evaluated the extracted causal relations. 展开更多
关键词 Linked data Social problem Causality extraction Crowdsourcing Text mining Open city data Semantic inference SPARQL Semantic Web Rule Language(swrl)
原文传递
上一页 1 下一页 到第
使用帮助 返回顶部