# Intent-Driven Smart Manufacturing Integrating Knowledge Graphs and Large Language Models

arXiv: https://arxiv.org/abs/2602.12419 · HTML: https://mlsanity.com/paper/2602.12419/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 12 Feb 2026.

## Builds on (most cited first, 5 of 5)

- [Exploring Large Language Models for Knowledge Graph Completion](https://mlsanity.com/paper/2308.13916/) (2023)
- [KARMA: Leveraging Multi-Agent LLMs for Automated Knowledge Graph Enrichment](https://mlsanity.com/paper/2502.06472/) (2025)
- [Mapping Text to Knowledge Graph Entities using Multi-Sense LSTMs](https://mlsanity.com/paper/1808.07724/) (2018)
- [The Semantic Asset Administration Shell](https://mlsanity.com/paper/1909.00690/) (2019)
- [Applying Large Language Models in Knowledge Graph-based Enterprise Modeling: Challenges and Opportunities](https://mlsanity.com/paper/2501.03566/) (2025)

## Abstract (arXiv)

The increasing complexity of smart manufacturing environments demands interfaces that can translate high-level human intents into machine-executable actions. This paper presents a unified framework that integrates instruction-tuned Large Language Models (LLMs) with ontology-aligned Knowledge Graphs (KGs) to enable intent-driven interaction in Manufacturing-as-a-Service (MaaS) ecosystems. We fine-tune Mistral-7B-Instruct-V02 on a domain-specific dataset, enabling the translation of natural language intents into structured JSON requirement models. These models are semantically mapped to a Neo4j-based knowledge graph grounded in the ISA-95 standard, ensuring operational alignment with manufacturing processes, resources, and constraints. Our experimental results demonstrate significant performance gains over zero-shot and 3-shots baselines, achieving 89.33% exact match accuracy and 97.27% overall accuracy. This work lays the foundation for scalable, explainable, and adaptive human-machine