General fuzzy automata under semantically similar environment: an application to enhanced search engine performance

Abstract

Automata theory provides a systematic framework for modelling computational processes; however, many real-world systems require greater adaptability and flexibility. This paper presents a general fuzzy automata model (GF ASE) designed to address these challenges, particularly in environments where the set of states is not predetermined. The proposed model is capable of dynamically integrating semantically similar states based on contextual relevance, enhancing its robustness in handling real-world data variability. Algorithms are introduced to measure the similarity between states. This advancement extends the capabilities of existing General Fuzzy Automata (GFA) frameworks, allowing them to function effectively in uncertain or evolving environments. A practical application of this model is demonstrated in the domain of search engine performance, where the proposed approach improves the handling of queries by mapping them to semantically related states, thereby enhancing the relevance and accuracy of results.