Traceable Knowledge Graph Reasoning Enables LLM-Assisted Decision Support for Industrial VOCs in the Steel Industry
arXiv:2605.27071v1 Announce Type: new Abstract: Key knowledge for steel-industry volatile organic compounds (VOCs) governance is scattered across unstructured scientific literature, making it difficult to integrate process, pollutant, and control-technology evidence and increasing the risk of hallucination when general large language models (LLMs) answer low-frequency industrial questions. Here we developed Chat-ISV, a knowledge graph (KG) enhanced multi-agent Q&A system that parses a curated steel-industry VOCs literature corpus, constructs a Neo4j KG with 27180 nodes and 81779 semantic edges
The increasing maturity of large language models and knowledge graph technologies allows for their practical application in complex industrial problem-solving, such as VOC governance.
This development indicates a tangible path for LLMs to provide decision support in highly specialized industrial sectors, mitigating hallucination risks through structured knowledge integration.
Traditional unstructured industrial knowledge can now be systematically leveraged by AI, moving beyond general LLM limitations to provide traceable and reliable insights for specific, low-frequency problems.
- · Industrial operators (steel sector)
- · AI agents developers
- · Knowledge graph platform providers
- · Environmental compliance technology
- · Traditional environmental consulting
- · General-purpose LLMs without domain-specific integration
Domain-specific LLM applications become more reliable and widely adopted in industrial settings due to reduced hallucination rates.
Increased efficiency and compliance in industries dealing with complex environmental regulations and technical knowledge.
The development of a new class of AI-powered industrial knowledge engineering roles and integrated decision-support systems across various heavy industries.
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Read at arXiv cs.AI