Queries to Non-Relational Data using Natural Language based on a Large Language Model
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Abstract
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.
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References
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