@inproceedings{park-etal-2026-ontology,
title = "Ontology-Based Knowledge Graphs for Industrial Standards with Scope-Aware Logical Rule Structuring",
author = "Park, Jiin and
Jeon, Hyuna and
Lee, Yoonseo and
Kim, Misuk",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1456/",
pages = "33282--33311",
ISBN = "979-8-89176-459-0",
abstract = "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"
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<abstract>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</abstract>
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%0 Conference Proceedings
%T Ontology-Based Knowledge Graphs for Industrial Standards with Scope-Aware Logical Rule Structuring
%A Park, Jiin
%A Jeon, Hyuna
%A Lee, Yoonseo
%A Kim, Misuk
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F park-etal-2026-ontology
%X 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
%U https://aclanthology.org/2026.emnlp-main.1456/
%P 33282-33311
Markdown (Informal)
[Ontology-Based Knowledge Graphs for Industrial Standards with Scope-Aware Logical Rule Structuring](https://aclanthology.org/2026.emnlp-main.1456/) (Park et al., EMNLP 2026)
ACL