Urban soil contamination poses persistent risks to redevelopment, public health, and ecological restoration, yet actionable evidence is scattered across site investigation reports, monitoring databases, and regulatory documents. Existing decision-support tools often depend on manual searches and provide limited structured reasoning. This study develops a domain knowledge graph (KG) and a KG-powered question-answering (KBQA) system for urban soil management to organize multi-source evidence and deliver precise, auditable answers to parcel- and pollutant-specific queries. The approach (1) defines an urban soil ontology covering parcels, land uses, pollutants, measurements, pathways, and regulatory thresholds; (2) extracts and links entities and relations from textual and tabular sources; (3) constructs a graph database with provenance; and (4) implements a KBQA pipeline that maps natural-language questions to constrained graph queries and verbalizes results with citations. The resulting system supports source identification, land-use-specific exceedance checks, affected-parcel listing, and remediation reference retrieval. Experiments on a curated QA set and a South China case study show higher answer accuracy and lower latency than text-only baselines, while consistently returning traceable evidence and reducing cross-document lookup effort. Compared to text-only RAG baselines, the KG-powered system achieved a 0.14 improvement in Exact Match scores (e.g., 0.81 vs. 0.58 for Threshold tasks) and maintained a competitive median latency of 0.75 s. The pipeline utilizes a 13B-parameter instruction-tuned LLM. The ontology, schema, benchmark QA sets, and sample queries are publicly released to support transfer to other regions.