The issues of creation of machine-understandable smart standards based on knowledge graphs

The development of digital transformation requires the widespread use of digital technologies in standardization documents. One of the goals is to create standards with machine-understandable content that will allow the use of digital documents at various stages of development and production without the need for a human operator. The purpose of this work is to describe an approach for creating and translating industry normative documents into a machine-understandable representation for their further use in software services and systems. There are three types of SMART standard content: machine-readable, machine-interpretable, and machine-understandable. Knowledge graphs are actively used to formalize data and knowledge when solving various problems. The new two-level approach is proposed for the creation and translation into a machine-understandable representation of regulatory documents as knowledge graphs. The approach defines two types of interpretation of a smart document (human readability and machine understandability) through two related formats: a graph, each semantic node of which represents text in a natural language, and a network of concepts and strict connections. Each node of a human-readable graph corresponds (in general) to a subtree of a machine-readable knowledge graph. As the basis for ensuring the transformation of one form of smart standard representation into another form, LLM models are used, supplemented by a specialized adapter obtained as a result of additional training using the Parameter-Efficient Fine-Tuning approach. Requirements have been established for a set of problem- and subject-oriented tools for generating knowledge graphs. The conceptual architecture of the system for supporting the solution of a set of problems based on knowledge graphs is shown, and the principles for implementing software components that work with smart knowledge for intelligent software services are established.