  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.
