ГИБРИДНЫЙ АЛГОРИТМ ПОИСКА НА ОСНОВЕ ФОРМАЛИЗМА ИНТУИЦИОНИСТСКИХ НЕЧЕТКИХ МНОЖЕСТВ

Татьяна Михайловна Леденёва; Григорий Дмитриевич Коваль

doi:10.17308/sait/1995-5499/2026/1/99-113

Authors

Tatiana M. Ledeneva Voronezh State University
Grigory D. Koval Voronezh State University

DOI:

https://doi.org/10.17308/sait/1995-5499/2026/1/99-113

Keywords:

fuzzy search, intuitionistic fuzzy sets, semantic embeddings, sentence transformers, hybrid algorithm, Levenshtein distance, natural language processing, information retrieval, relevance assessment

Abstract

The article is devoted to the urgent problem of improving the efficiency of information retrieval under conditions of multidimensional uncertainty caused by spelling errors, syntactic variations and semantic differences in natural language texts. Existing approaches - syntactic (Levenshtein distance) and semantic (modern sentence embeddings) - demonstrate limited effectiveness in mixed scenarios, which necessitates their deep integration. The scientific novelty of the research lies in the development of a hybrid algorithm that synthesizes fuzzy search methods based on the formalism of intuitionistic fuzzy sets (IFS). An original mathematical model is proposed that transforms syntactic and semantic similarity metrics into consistent membership and non-membership functions of IFS, allowing to aggregate heterogeneous evidence “for” and “against” relevance and to quantify the degree of uncertainty. An algorithm architecture with independent modules of syntactic and semantic analysis is developed; their results are integrated using IFS operations. Experimental verification was carried out on the RussianSuperGLUE benchmark data (TERRa, LiDiRus datasets). The results confirmed the advantage of the proposed method in terms of Precision@k, MAP and NDCG@k metrics compared to basic syntactic, semantic and classical fuzzy approaches, especially in cases with mixed uncertainty. The practical significance of the work lies in the possibility of using the algorithm as a core of intelligent search systems for digital libraries, corporate document management and support systems, where resistance to errors and the ability to take into account semantic proximity are critically important.

Author Biographies

Tatiana M. Ledeneva, Voronezh State University

DSc in Technical Sciences, Professor, Head of the Department of Computational Mathematics and Applied Information Technologies, Voronezh State University
Grigory D. Koval, Voronezh State University

2nd year master’s student at the Department of Computational Mathematics and Applied Information Technologies, Voronezh State University

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