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.