Leveraging Large Language Models for Data Service Discovery

In the context of the Internet of Services paradigm for Industry 4.0, data services can be discovered and composed to accomplish different data analytics scenarios amongst the actors of a production network. Recently, Large Language Models (LLMs) have been increasingly considered for service discovery and composition as a promising alternative to previous approaches that often require substantial effort to produce formal service descriptions and/or annotations. In this paper, we introduce some exploratory experiments on the use of an LLM-based system for the discovery of data services to fulfil data analysis scenarios. First, a data service model, that represents in a declarative way data service operations, is provided. Then, we propose prompt templates for the interaction with the LLM-based system, that leverages the service model, aimed at reducing trial-and-error interactions for identifying potential service candidates. The effectiveness of the approach is being assessed in a real-world case study of a research project