Large Language Models (LLMs) can match local semantic clues in knowledge graphs (KGs), but complex KGQA often requires satisfying the full dependency structure of a question. Existing retrieval-based methods therefore risk producing reasoning paths that look plausible at each step but violate the query’s global intent. We propose Guiding Graph, a schema-light query-structure interface that turns KGQA from open-ended semantic traversal into structure-constrained query completion. A Guiding Graph exposes the entities, placeholders, and global dependencies that KG exploration must preserve, serving as an operational interface between natural language and graph traversal. The interface is training-free by default through prompt-based construction, and can optionally be strengthened with a cold-start fine-tuned constructor trained from limited bootstrapped supervision. Given the constructed Guiding Graph, exploration is governed by target selection from the query structure, structure-guided pruning before semantic matching, and target-scoped semantic alignment under the current Guiding Graph triple. Experiments on WebQSP, CWQ, and a domain-specific agricultural KG show that explicit query-structure guidance improves accuracy, robustness, and efficiency, with especially clear benefits for multi-hop reasoning and smaller LLMs.