Access automation to information for navigating through semantic library data and integrating the knowledge graph with the language model

The paper studies the problem of integrating language models (LM) and knowledge graph (KG). KG is built in the semantic library of scientific subject areas LibMeta for navigation through scientific publications. Using the example of KG of the mathematical subject area (SjD), it is shown that as a result of this approach, LM does not go beyond the SjD, which allows us to state a more relevant answer to the query. The descriptions of mathematical SjD are based on mathematical encyclopedias of the soviet mathematical school and the library of subject areas is filled by integrating subject areas of specialized mathematical journals. Using the example of mathematical SjD and applications, the problem of creating an environment for using a digital assistant in Russian when mastering scientific knowledge in a local SjD and accessing scientific research is considered. Setting up LM on SjD is implemented by creating a set of instructions and checking the truth of the answers based on them. Applications of the research results are expected to be implemented in mathematical knowledge systems, library and journal systems to support business processes, search and analysis of scientific publications. The research is aimed at creating a technology for information support of scientific research in the process of searching and analyzing scientific information. The proposed approach allows reducing the flow of information noise when working with scientific publications. A methodology for the interaction of LM and KG of mathematical SjD has been developed based on instructions applied to the description of SjD in the form of KG. The application of the proposed approach will allow using multiple instructions to simplify work with LM in the process of searching for specialized information while reducing LM hallucinations and without involving expert advice. In the context of intensification of scientific work associated with an increasing flow of information, a solution for search augmented generation (RAG) is proposed.