Urban areas have many problems such as homelessness, illegally parked bicycles, 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. Therefore, we propose constructing an urban problem linked open data (LOD) system that would include urban problems' causality. In addition, we propose a method for detecting vicious cycles of urban problems using inferences from the LOD. We first designed a Linked Data schema that represents urban problems' causality. Next, we instantiated actual causes and effects using crowdsourcing, supported with techniques based on natural language processing. In addition, we complemented the constructed LOD by drawing inferences using Semantic Web Rule Language (SWRL) rules. Finally, using SPARQL queries, we detected several root problems that led to vicious cycles, then urban-problem experts evaluated the extracted causal relations.