Ontology-Based Knowledge Graph Framework for Industrial Standard Documents via Hierarchical and Propositional Structuring

2025-12-09

AI Summary

Industrial standards pose significant challenges for knowledge modeling due to their broad technical scope, intricate regulatory logic, and heterogeneous structure—including tables, constraints, exceptions, and numerical computations. To address this, we propose a hierarchical propositionalization-based joint modeling approach that, for the first time, unifies conditional logic, numerical rules, and tabular semantics within an ontology-enhanced knowledge graph (KG), substantially improving semantic expressivity for nested constraints and scoping relationships. Integrating large language model–driven triple extraction, structured table recognition, and ontology alignment, we construct a KG-RAG framework supporting multi-hop question answering and toxic clause detection. Evaluated on a novel, multi-type benchmark dataset, our method consistently outperforms existing KG-RAG approaches, demonstrating superior effectiveness and advancement in enhancing logical inferability and intelligent management of industrial standard knowledge.

Technology Category

• Knowledge Representation and Reasoning: Description Logics
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Ontology-based knowledge graph (KG) construction is a core technology that enables multidimensional understanding and advanced reasoning over domain knowledge. Industrial standards, in particular, contain extensive technical information and complex rules presented in highly structured formats that combine tables, scopes of application, constraints, exceptions, and numerical calculations, making KG construction especially challenging. In this study, we propose a method that organizes such documents into a hierarchical semantic structure, decomposes sentences and tables into atomic propositions derived from conditional and numerical rules, and integrates them into an ontology-knowledge graph through LLM-based triple extraction. Our approach captures both the hierarchical and logical structures of documents, effectively representing domain-specific semantics that conventional methods fail to reflect. To verify its effectiveness, we constructed rule, table, and multi-hop QA datasets, as well as a toxic clause detection dataset, from industrial standards, and implemented an ontology-aware KG-RAG framework for comparative evaluation. Experimental results show that our method achieves significant performance improvements across all QA types compared to existing KG-RAG approaches. This study demonstrates that reliable and scalable knowledge representation is feasible even for industrial documents with intertwined conditions, constraints, and scopes, contributing to future domain-specific RAG development and intelligent document management.

Problem

• Constructing knowledge graphs from structured industrial standards
• Extracting hierarchical and logical structures from complex documents
• Improving QA performance with ontology-aware KG-RAG framework

Innovation

• Hierarchical semantic structuring of industrial standards
• LLM-based triple extraction for ontology-knowledge graph integration
• Ontology-aware KG-RAG framework for improved QA performance