Mun Yong Yi

Korea Advanced Institute of Science and Technology

Papers

Total Citations

H-Index

About

Mun Yong Yi is a distinguished researcher whose work bridges information retrieval, web crawling, and artificial intelligence. His key contributions lie in developing innovative methodologies to improve the efficiency and accuracy of focused web crawlers—systems that selectively harvest domain-specific information from the vast expanse of the internet. Yi’s most-cited paper, "An effective approach to enhancing a focused crawler using Google" (2019), has garnered 13 citations, demonstrating its influence in the field. In this work, he introduced a novel strategy that leverages Google’s search engine to guide crawlers toward more relevant pages, significantly reducing computational overhead while boosting precision. This approach has practical implications for data mining, digital libraries, and knowledge discovery, offering a scalable solution for researchers and practitioners seeking to extract targeted information from the web. Beyond this notable achievement, Yi’s broader research portfolio explores the intersection of machine learning and information systems, aiming to create smarter, more adaptive tools for navigating digital landscapes. His work continues to inspire advancements in automated data collection and web intelligence, making him a valuable contributor to the evolution of search and retrieval technologies.