Traceable Knowledge Graph Reasoning Enables LLM-Assisted Decision Support for Industrial VOCs in the Steel Industry
A new system called Chat-ISV has been developed to assist in decision-making regarding volatile organic compounds (VOCs) in the steel industry. This system utilizes a knowledge graph to integrate fragmented information from scientific literature, enhancing the reliability of responses to industrial queries. Benchmark tests indicate that Chat-ISV significantly improves factual accuracy and provides a scalable solution for pollution control in specialized industrial domains.
- ▪Chat-ISV is a knowledge graph enhanced multi-agent Q&A system designed for the steel industry.
- ▪It constructs a Neo4j knowledge graph with over 27,000 nodes and 81,000 semantic edges from curated VOCs literature.
- ▪The system achieved a precision of 96.93% and an F1-score of 0.830 in benchmark tests.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.27071 |
| Publication time | Wed, 27 May 2026 00:00:00 -0400 |
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Computer Science > Artificial Intelligence arXiv:2605.27071 (cs) [Submitted on 26 May 2026] Title:Traceable Knowledge Graph Reasoning Enables LLM-Assisted Decision Support for Industrial VOCs in the Steel Industry Authors:Changqing Su, Yu Ding, Zuhong Lin, Hongyu Liu, Xi He, Zheng Zeng, Liqing Li View a PDF of the paper titled Traceable Knowledge Graph Reasoning Enables LLM-Assisted Decision Support for Industrial VOCs in the Steel Industry, by Changqing Su and 6 other authors View PDF HTML (experimental) 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…
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