Queries to Nonrelational Data Using Natural Language Based on a Large Language Model

The main purpose of this work is to explore new opportunities for the organization of 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, has also been noted. The widespread use of large language models for query generation in various language settings and fields of expertise over the last two years has been demonstrated. A study was conducted to explore the potential of the AllegroGraph graph database for using large language models for natural language search. The functionality of the database was examined using the example of a metadata system for thermophysical properties in the form of Thermal domain ontology. Testing search queries in a bilingual (English and Russian) database environment 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.