Systematic Mapping of Computational Linguistics in Distributed Knowledge Based Systems and Management
Аннотация
Advancements in computational linguistics have enabled the formulation of multiple natural language processing frameworks with considerable semantic accuracy, knowledge representation, and real-time adaptability benefits. Emerging research on distributed knowledge-based systems is challenging traditional conceptions of language processing and data integration, and in the process, opening up windows of opportunity for enhancing the scalability associated with knowledge management in decentralized environments. As little is known about where computational linguistics integration is gaining momentum beyond academic research and software engineering, the purpose of this systematic mapping study is to map in what areas of distributed knowledge management it is perceived to gain traction. Drawing on data from 150 systematically selected research articles and trend analysis in computational linguistics applications, we identify a long tail of application domains and methodological approaches in which a total of 42 unique computational models operate, including techniques such as knowledge graph embeddings, transformer-based architectures, and multimodal language models. Our findings reveal a strong, positive correlation coefficient (r = 0.82) between natural language processing adoption and knowledge retrieval efficiency in distributed systems. However, existing frameworks do not passively comply. Rather, their linguistic adaptability and semantic interpretation mechanisms are integrated into the core functionality of distributed knowledge networks. The study concludes by identifying key research gaps, reflecting on the application of machine learning-enhanced linguistic models in the field of knowledge management, and proposing suggestions for future research directions in distributed data processing. The findings enrich understandings of the workings of computational linguistics methodologies in experiences of real-time knowledge extraction and intelligent information retrieval.