The Semantic Web Lab (SWlab) at the University of Zakho has made significant contributions to the field of document clustering with the publication of a comprehensive systematic review paper in the The International Conference on Innovations in Computing Research.
Authored by Saad Hikmat Haji, Karwan Jacksi, and Razwan Mohmed Salah, the paper, titled “Systematic Review for Selecting Methods of Document Clustering on Semantic Similarity of Online Laboratories Repository,” investigates the challenges and opportunities of organizing and retrieving information from online laboratory repositories.
The researchers conducted a thorough review of 25 relevant research papers published in leading academic journals, focusing on document clustering methods that leverage semantic similarity. Traditional clustering algorithms often fall short in accurately representing the true meaning of documents due to their limited ability to consider the semantic relationships between words.
This systematic review analyzes various approaches to incorporating semantic information into document clustering, including the use of techniques like TF-IDF and Word Embeddings. By examining the strengths and weaknesses of different methods, the researchers aim to provide valuable insights into selecting the most effective clustering techniques for organizing and retrieving information from online laboratory repositories.
This research has significant implications for improving the accessibility and usability of online laboratory resources, enabling researchers to more easily find relevant information and collaborate effectively.
For further details and access to the full paper, please refer to:
- Publication: The International Conference on Innovations in Computing Research
- Pages: 239-252
- Authors: Saad Hikmat Haji, Karwan Jacksi, Razwan Mohmed Salah
- Publisher: Springer International Publishing