VECTOR SEARCH IN THE ERA OF SEMANTIC UNDERSTANDING: A COMPREHENSIVE REVIEW OF APPLICATIONS AND IMPLEMENTATIONS
Keywords:
Vector Search, Semantic Embeddings, Information Retrieval, Neural Language Models, Cross-Domain ApplicationsAbstract
The transition from token-based to vector search represents a significant evolution in information retrieval technology, fundamentally transforming how systems understand and process user queries. This comprehensive article review examines the architectural foundations of vector search, analyzing how neural networks convert both queries and documents into dense vector embeddings that capture semantic relationships. The article explores the implementation of vector search across multiple domains, including e-commerce, academic research, healthcare, enterprise knowledge management, media streaming, legal research, and cultural heritage institutions. The article presents a systematic analysis of vector search applications, examining technical implementations, performance metrics, and user satisfaction outcomes across these sectors. The findings demonstrate consistent improvements in discovery accuracy and user satisfaction compared to traditional search methods. This article also addresses the technical challenges of implementing vector search at scale, including indexing strategies and optimization techniques. Finally, The article discusses emerging applications and future directions for this technology, providing insights for researchers and practitioners in the field of information retrieval.
References
G. Poveda, A. Westerski, and C. A. Iglesias, "Application of semantic search in Idea Management Systems," 2012 International Conference for Internet Technology and Secured Transactions, IEEE Xplore, Dec. 2012. https://ieeexplore.ieee.org/document/6470949
G. Samardzhiev and M. Nisheva-Pavlova, "Application of Machine Learning and Natural Language Processing in Semantic Search Systems," 2023 International Conference on Big Data, Knowledge and Control Systems Engineering (BdKCSE), IEEE Xplore, Nov. 2023. [Online]. Available: https://ieeexplore.ieee.org/document/10339730
T. D. Nguyen and S. Tirthapura, "V2V: Vector Embedding of a Graph and Applications," 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Vancouver, BC, Canada, 2018, pp. 1-8, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8425549
G. Orchard, R. Benosman, R. Etienne-Cummings, and N. V. Thakor, "A spiking neural network architecture for visual motion estimation," 2013 IEEE Biomedical Circuits and Systems Conference (BioCAS), Rotterdam, Netherlands, 2013, pp. 1-4, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/6679698
M. Staudinger et al., "Reproducible Query Processing and Data Citation of in Situ Soil Moisture Data," 2023 IEEE 19th International Conference on e-Science (e-Science), [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10254929
T. Connors and D.A. Schneider, "The Papyrus Query Processing Engine," 1992 Proceedings of the Second International Workshop on Research Issues on Data Engineering: Transaction and Query Processing, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/227425
A. Mutemi and F. Bacao, "E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review," IEEE Transactions on Big Data, 7(2), 419-444, 2024. https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?arnumber=10506811
S. Imran, T. Mahmood, A. Morshed, and T. Sellis, "Big Data Analytics in Healthcare—A Systematic Literature Review and Roadmap for Practical Implementation," IEEE/CAA Journal of Automatica Sinica, 8(1), 1-22, 2021. https://ieee-jas.net/article/doi/10.1109/JAS.2020.1003384?pageType=en
M. M. Vlajnic and S. J. Simske, "Accuracy and Performance of Machine Learning Methodologies: Novel Assessments of Country Pandemic Vulnerability Based on Non-Pandemic Predictors," IEEE Access, 2021. [Online]. Available: https://ieeexplore.ieee.org/document/10225741
Suparna N and Manjaiah DH, "Implementation and Performance Analysis of IDEA in IoT," 2022 International Conference on Artificial Intelligence and Data Engineering (AIDE). [Online]. Available: https://ieeexplore.ieee.org/document/10060851
Y. Liu et al., "Federated learning for 6G communications: Challenges, methods, and future directions," China Communications, 2021. [Online]. Available: https://ieeexplore.ieee.org/document/9205981
