The Semantic Web Lab (SWlab) at the University of Zakho has announced the completion of its first Master’s thesis, a groundbreaking work on “Semantic Similarity for Document Clustering using TFIDF and K-mean.” Authored by Rowayda Ibrahim, under the guidance of SR Zeebaree and Karwan Jacksi, the thesis delves into the critical area of semantic analysis and its applications in document organization.
The thesis presents a novel approach to document clustering, leveraging the power of Term Frequency-Inverse Document Frequency (TFIDF) and the K-means algorithm. By analyzing the semantic relationships between words and concepts within a document, the researchers have developed a robust system capable of grouping similar documents together, regardless of minor variations in wording or phrasing.
This research has significant implications for various fields, including information retrieval, text mining, and natural language processing. By effectively clustering documents, organizations can streamline information management, improve search accuracy, and gain deeper insights from vast collections of textual data.
The SWlab at the University of Zakho is committed to advancing research in semantic technologies and their applications. This pioneering thesis marks a significant milestone in their efforts to contribute to the growing field of semantic web research.