A method of semantic search for educational content based on multi-agent technologies

The digital transformation of industry is accompanied by the active adoption of new technologies and the rapid evolution of production processes. A significant portion of educational materials is distributed across various information sources, including internal corporate systems, open educational platforms, and specialized web resources. Such resources often contain duplicates, redundant information, and heterogeneous metadata, which complicates the timely retrieval of relevant learning materials. The subject of the study is a method of semantic search for educational content in a distributed information environment using ontology-based knowledge models. The goal of the work is to investigate a method of semantic search for educational content in a distributed information environment based on a multi-agent organization of information resource processing and the use of ontology-based knowledge models. The objectives of the study are: to investigate the architectural model of a multi-agent search system; to develop a semantic selection algorithm based on the comparator identification method and an ontology-based model; to formalize a relevance evaluation predicate considering weighted metadata coefficients; to develop a multi-agent software platform; and to experimentally evaluate performance and resource consumption under different agent operating modes. Research methods include: the method of multi-agent organization of information resource processing with non-blocking message exchange; three-level URL deduplication; ontology-based term matching and a formalized relevance evaluation predicate; and experimental measurement of processing time, the number of processed links, and system resource consumption. Results: a model of a multi-agent system with four types of agents and a semantic search algorithm eliminating loops and duplicate links has been proposed; a software platform based on Kotlin using coroutines and asynchronous interaction between agents has been implemented; experimental results demonstrate that the proposed organization of processing provides higher performance compared to the sequential mode. Conclusions: the integration of semantic search and a multi-agent architecture enables efficient organization of the process of discovering and processing educational content in a distributed environment. The proposed method ensures coordinated operation of agents, eliminates link duplication, and provides a rational balance between search completeness and the use of computational resources.