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

Time-Series Question Answering (TSQA) aims to interpret time-series information and generate answers according to the intent of natural language questions. However, in TSQA, the required temporal granularity and reasoning scope vary across queries, making it crucial to effectively organize the time-series information needed for each query. In this study, we propose TiMER (**Ti**me-series and language alignment with **M**ulti-granularity **E**xpert **R**outing), a time-series-language alignment framework that selectively constructs multi-granularity time-series information according to query intent and connects it to the LLM input space. Experimental results on a large-scale TSQA benchmark show that TiMER achieves an overall accuracy of 81.02% using only a small number of external alignment modules while keeping the LLM frozen, outperforming various LLM-based baselines. In addition, extensive ablation studies verify the effectiveness of each component. This study presents an effective direction for natural language-based time-series understanding and temporal reasoning by dynamically organizing and utilizing time-series information according to query intent. The code is publicly available at https://github.com/hyuna0926/TiMER_paper.