The semantic similarity measures using Arabic ontology
Other Title(s)
مقاييس التشابه الدلالي باستخدام الانتولوجي العربي
Dissertant
Thesis advisor
Comitee Members
al-Sadi, Jihad
Abu Shurayhah, Ahmad
University
Middle East University
Faculty
Faculty of Information Technology
Department
Computer Science Department
University Country
Jordan
Degree
Master
Degree Date
2017
English Abstract
The semantic similarity measures have been used in many applications including information retrieval and natural language processing.
There are many measures that use a lexical database such as WordNet to calculate the similarity between English concepts.
However, few researches have been studied semantic similarity measures using Arabic WordNet.
The traditional semantic similarity measures were classified into four categories: path-based measures, information content-based, feature-based measures, and hybrid measures.
Several measures from different categories have been applied on Arabic WordNet to which measure has the best performance using Arabic WordNet.
Human benchmark has been used to evaluate the performance of these measures over Arabic WordNet.
Experimental results show that the WuP measure has achieved the minimum mean square error (MSE) with value of (1.64%), and highest value of correlation coefficient with human ratings (0.92).
These results indicate that WuP measure has the best performance on Arabic WordNet compared to other measures.
Also, the results show that PATH measure has the worst performance.
This thesis proposed a new semantic similarity measure using the taxonomy of Arabic WordNet.
The new measure takes three factors into account: depth of concepts in Arabic WordNet tree, distance between two compared concepts and information content of the least common concept that subsumed two compared concepts.
The weight of these factors can be adapted manually.
However, several experiments have been conducted to find the best weight that achieves the minimum MSE.
In order to evaluate the new measure, the Arabic dataset that used previously to evaluate the measures has been used to test the new measure.
Then, the results of applying new measure over Arabic WordNet have been compared with the results of the other measures.
However, the results showed that the new measure has achieved the highest correlation coefficient with human ratings (0.96), furthermore, the new measure has obtained a very good value of MSE (1.89%) compared with the other measures
Main Subjects
Information Technology and Computer Science
Topics
No. of Pages
87
Table of Contents
Table of contents.
Abstract.
Abstract in Arabic.
Chapter One : Introduction.
Chapter Two : Literature review and related works.
Chapter Three : Experimental work and new proposed measure.
Chapter Four : Experimental results and measures evaluation.
Chapter Five : Conclusions and future work.
References.
American Psychological Association (APA)
al-Dayri, Muhammad Ghandi. (2017). The semantic similarity measures using Arabic ontology. (Master's theses Theses and Dissertations Master). Middle East University, Jordan
https://search.emarefa.net/detail/BIM-762685
Modern Language Association (MLA)
al-Dayri, Muhammad Ghandi. The semantic similarity measures using Arabic ontology. (Master's theses Theses and Dissertations Master). Middle East University. (2017).
https://search.emarefa.net/detail/BIM-762685
American Medical Association (AMA)
al-Dayri, Muhammad Ghandi. (2017). The semantic similarity measures using Arabic ontology. (Master's theses Theses and Dissertations Master). Middle East University, Jordan
https://search.emarefa.net/detail/BIM-762685
Language
English
Data Type
Arab Theses
Record ID
BIM-762685
