Industrial standard documents combine hierarchical section structures, table-based rules, and complex conditional statements; as a result, the same expression can refer to different rules depending on where it appears in a document. However, existing text-based approaches and conventional knowledge graph (KG) construction methods do not jointly preserve these structures, making them brittle on complex rule-based queries. In this work, we propose an ontology-based KG construction method that formalizes section structure as an ontology and integrates it with rule-aware triplets extracted from text and tables. To evaluate our approach, we construct IndusSpec-QA, a QA dataset based on three industrial standard documents, and compare the proposed method against a range of baselines. Experimental results show that the proposed Ontology-based KG-RAG outperforms existing methods, with particularly large gains on table-based rule questions and multi-condition reasoning. These results demonstrate the importance of knowledge representations that jointly preserve section and rule structures for QA over industrial standard documents. The code is publicly available at: https://github.com/Ontology-KG/ontology_based_kg_paper