Detecting Vicious Cycles in Urban Problem Knowledge Graph using Inference Rules

Abstract

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 proposes constructing an urban problem knowledge graph that would include urban problems' causality and the related cost information in budget sheets. In addition, this paper proposes 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.

Sustainable and Equitable Land Management

“…They framed their socio-demographic change model as a link prediction task (Liu and De Sabbata 2020). Egami et al (2021) used knowledge graphs and inference rules "[…] to detect vicious cycles from among urban problems, to identify these cycles' root problems, and to search the related budget information using the constructed KG." The detected vicious cycles and the root problems were estimated using SPARQL queries and evaluated with domain experts (Egami et al 2021).…”

“…Egami et al (2021) used knowledge graphs and inference rules "[…] to detect vicious cycles from among urban problems, to identify these cycles' root problems, and to search the related budget information using the constructed KG." The detected vicious cycles and the root problems were estimated using SPARQL queries and evaluated with domain experts (Egami et al 2021). Pu et al (2021) used a spatiotemporal migration knowledge graph framework based on the Global Migration Dataset and the Baidu Encyclopedia to "[…] quickly and accurately obtain the comprehensive knowledge of migration, significantly improve the ability of human being to apply and analyse the knowledge and data of migration, which has wide theoretical and practical value in the field of migration."…”