With the advent of network science, it has become possible to explore complex network systems, including social and information networks, by presenting them as graph models. The exponential growth of the total volume of scientific publications determines the relevance of the tasks of analyzing their interrelations. In network science, models and methods related to the field of so-called citation networks are being developed to solve these problems. However, network metrics are not used when analyzing publications in citation databases. The paper considers the issues of creating a decision support system for the selection of information sources based on data on the citation of scientific publications. A software package has been developed for making decisions on determining an important publication in a certain thematic area. The software package is based on a method of ranking publications by importance based on the analysis of citation networks, which allows you to identify publications that do not clearly stand out when ranking based on known bibliometric indicators or known measures of centrality of nodes in their pure form. A study and comparative analysis of software for visualization and research of all types of graphs and social networks has been conducted. Studies have been carried out confirming the effectiveness of the proposed decision support system in the selection of information sources.

The main purpose of this work is to explore new opportunities for organizing natural language queries in scientific local databases that are not relational. A brief review of recent research shows that there has been an active introduction of natural language queries into databases of various types, and the use of machine learning methods, such as neural algorithms, is noted. The widespread use of large language models in the last two years for query generation in various language settings and fields of expertise has been demonstrated. A study has been conducted to explore the potential of the AllegroGraph graph database in using large language models for natural language search. The functionality of the database has been examined using the example of a metadata system for thermophysical properties in the form of the "Thermal" domain ontology. Testing search queries in a bilingual (English and Russian) database environment has revealed some general problems that can be overcome, and it gives us good hope for the future application of new services using large language models.