# Generation of Asset Administration Shell with Large Language Model Agents: Toward Semantic Interoperability in Digital Twins in the Context of Industry 4.0

arXiv: https://arxiv.org/abs/2403.17209 · HTML: https://mlsanity.com/paper/2403.17209/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 9 of its 38 references that are in the map.
- First posted on arXiv 25 Mar 2024.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Mixtral of Experts](https://mlsanity.com/paper/2401.04088/) (2024)
- [WizardLM: Empowering large pre-trained language models to follow complex instructions](https://mlsanity.com/paper/2304.12244/) (2023)
- [A Primer in BERTology: What we know about how BERT works](https://mlsanity.com/paper/2002.12327/) (2020)
- [Finding Neurons in a Haystack: Case Studies with Sparse Probing](https://mlsanity.com/paper/2305.01610/) (2023)
- [Learning to Generate Reviews and Discovering Sentiment](https://mlsanity.com/paper/1704.01444/) (2017)
- [Towards autonomous system: flexible modular production system enhanced with large language model agents](https://mlsanity.com/paper/2304.14721/) (2023)
- [Toward a Mapping of Capability and Skill Models using Asset Administration Shells and Ontologies](https://mlsanity.com/paper/2307.00827/) (2023)

## Built on by (most cited first, 8 of 8)

- [From Language to Action: A Review of Large Language Models as Autonomous Agents and Tool Users](https://mlsanity.com/paper/2508.17281/) (2025)
- [LLM experiments with simulation: Large Language Model Multi-Agent System for Simulation Model Parametrization in Digital Twins](https://mlsanity.com/paper/2405.18092/) (2024)
- [On the Use of Large Language Models to Generate Capability Ontologies](https://mlsanity.com/paper/2404.17524/) (2024)
- [Toward a Method to Generate Capability Ontologies from Natural Language Descriptions](https://mlsanity.com/paper/2406.07962/) (2024)
- [FacProcessTwin: An LLM-Based System for Process Twin Development](https://mlsanity.com/paper/2606.17666/) (2026)
- [From Regulation to Implementation: A Critical Evaluation of LLM-Assisted Regulatory Compliance in Industry](https://mlsanity.com/paper/2608.21317/) (2026)
- [Quality Metrics for LLM-Generated Asset Administration Shells: A Perturbation-Based Evaluation Approach](https://mlsanity.com/paper/2609.07290/) (2026)
- [AAS-RAIL: Improving Information Extraction for Asset Administration Shells through Retrieval-Augmented In-Context Learning](https://mlsanity.com/paper/2609.07334/) (2026)

## Abstract (arXiv)

This research introduces a novel approach for achieving semantic interoperability in digital twins and assisting the creation of Asset Administration Shell (AAS) as digital twin model within the context of Industry 4.0. The foundational idea of our research is that the communication based on semantics and the generation of meaningful textual data are directly linked, and we posit that these processes are equivalent if the exchanged information can be serialized in text form. Based on this, we construct a "semantic node" data structure in our research to capture the semantic essence of textual data. Then, a system powered by large language models is designed and implemented to process the "semantic node" and generate standardized digital twin models from raw textual data collected from datasheets describing technical assets. Our evaluation demonstrates an effective generation rate of 62-79%, indicating a substantial proportion of the information from the source text can be translated error-free to the target digital twin instance model with the generative capability of large language models. This result has a direct application in the context of Industry 4.0, and the designed system is implemented as a data model generation tool for reducing the manual effort in creating AAS model. In our evaluation, a comparative analysis of different LLMs and an in-depth ablation study of Retrieval-Augmented Generation (RAG) mechanisms provide insights into the effectiveness of LLM systems for interpreting technical concepts and translating data. Our findings emphasize LLMs' capability to automate AAS instance creation and contribute to the broader field of semantic interoperability for digital twins in industrial applications. The prototype implementation and evaluation results are presented on our GitHub Repository: https://github.com/YuchenXia/AASbyLLM.