Detecting Vicious Cycles in Urban Problem Knowledge Graph using Inference Rules  

作　　者：Shusaku Egami Takahiro Kawamura Kouji Kozaki Akihiko Ohsuga 

机构地区：[1]National Institute of Advanced Industrial Science and Technology(AIST),Tokyo 135-0064,Japan [2]Graduate School of Informatics and Engineering,The University of Electro-Communications,Tokyo 182-8585,Japan [3]National Agriculture and Food Research Organization,Ibaraki 305-8517,Japan [4]Faculty of Information and Communication Engineering,Osaka Electro-Communication University,Osaka 572-8530,Japan

出　　处：《Data Intelligence》2022年第1期88-111,共24页数据智能（英文）

基　　金：supported by Japan Society for the Promotion of Science(JSPS)KAKENHI(No.16K12411,No.16K00419,No.16K12533,No.17H04705,and No.18J13988)

摘　　要：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.

关 键 词：Linked data Social problem Causality extraction Crowdsourcing Text mining Open city data Semantic inference SPARQL Semantic Web Rule Language(SWRL) 

分 类 号：D035[政治法律—政治学]