LLM-Supported Formal Knowledge Representation for Enhancing Control Engineering Content with an Interactive Semantic Layer
This addresses the problem of managing growing research output for control engineering researchers, though it appears incremental as it builds on existing frameworks.
The paper tackles the challenge of structuring and formalizing domain knowledge in control engineering by developing an LLM-supported method for semi-automated generation of formal knowledge representations, resulting in an interactive semantic layer that enhances source documents to facilitate knowledge transfer.
The rapid growth of research output in control engineering calls for new approaches to structure and formalize domain knowledge. This paper briefly describes an LLM-supported method for semi-automated generation of formal knowledge representations that combine human readability with machine interpretability and increased expressiveness. Based on the Imperative Representation of Knowledge (PyIRK) framework, we demonstrate how language models can assist in transforming natural-language descriptions and mathematical definitions (available as LaTeX source code) into a formalized knowledge graph. As a first application we present the generation of an ``interactive semantic layer'' to enhance the source documents in order to facilitate knowledge transfer. From our perspective this contributes to the vision of easily accessible, collaborative, and verifiable knowledge bases for the control engineering domain.