Karwan Jacksi

University of Zakho, Computer Science, Faculty Member

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Karwan Jacksi obtained a B.Sc. in Computer Science from the University of Duhok, Kurdistan Region - Iraq, in 2007. In 2011, he obtained an M.Sc. in Computer Science at Uppsala University, Sweden. He earned his Ph.D. degree in Computer Science from the University of Zakho (UoZ) and Eastern Mediterranean University in a split-site program, in 2018.
He is a lecturer at the department of Computer Science, UoZ. He is teaching graduate courses and supervising several graduate students. He is currently the vice president for student affairs at the UoZ.

Dr. Jacksi served as program co-chair of the 2nd International Conference on Advanced Science and Engineering (ICOASE2019), and a technical chair of the International Conference on Advanced Science and Engineering (ICOASE2018), 5th International Engineering Conference on Developments in Engineering Applications (IEC2019), Science Journal of University of Zakho (SJUOZ), and Humanities Journal of University of Zakho (HJUOZ). He has been an active member of several national and international journals, conferences, and workshops.
His areas of interest include semantic web, linked data, data science, information retrievals, and search engines. He has authored and co-authored 25 papers in international journals and conference proceedings.

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Survey on Semantic Similarity Based on Document Clustering

Clustering is a branch of data mining which involves grouping similar data in a collection known ... more Clustering is a branch of data mining which involves grouping similar data in a collection known as cluster. Clustering can be used in many fields, one of the important applications is the intelligent text clustering. Text clustering in traditional algorithms was collecting documents based on keyword matching, this means that the documents were clustered without having any descriptive notions. Hence, non-similar documents were collected in the same cluster. The key solution for this problem is to cluster documents based on semantic similarity, where the documents are clustered based on the meaning and not keywords. In this research, fifty papers which use semantic similarity in different fields have been reviewed, thirteen of them that are using semantic similarity based on document clustering in five recent years have been selected for a deep study. A comprehensive literature review for all the selected papers is stated. A comparison regarding their algorithms, used tools, and evaluation methods is given. Finally, an intensive discussion comparing the works is presented.

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Survey on Semantic Similarity Based on Document Clustering

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