Enhancing the ontologies matching in semantic web using artificial neural network
Other Title(s)
تحسين استجابة التجميعات الموجودة في الويب الدلالي عن طريق استخدام الشبكة العصبية الصناعية
Dissertant
Thesis advisor
University
Princess Sumaya University for Technology
Faculty
King Hussein Faculty for Computing Sciences
University Country
Jordan
Degree
Master
Degree Date
2018
English Abstract
Ontology matching process aims to find correspondence matching between similar terms in different ontologies related to the same domain.
The ontologies are the main component in the semantic web that provides a new framework consisting of many ontologies, for whose support specific languages were designed such as RDF, OWL, and XML.
The process of finding the relationships between multiple ontologies can be achieved manually, by an error-prone and tedious process, or automatically using rule-based techniques (e.g.
WordNet) and specific equations like edit distance, or learning-based techniques such as machine learning tools (e.g.
artificial neural network, ANN).
This study conducts automatic matching between ontologies.
Five large overlapping ontologies were selected: EKAW, CONFIOUS, CMT, COCUS, and CONFOF.
These ontologies are analyzed based on their constituent formulas and expressions, with schema and instance information used as final RDF-triplets datasets.
The final datasets are encoded manually using synset offset value from WordNet.
Finally, ANN is taken to find correspondence matching between the five ontologies.
Three training algorithms are used to train the ANN separately: gradient descent, conjugate gradient, and Levenberg-Marquardt.
The contribution of our approach is to retrieve more than output at the same time, which means that the ANN has to be trained enough to distinguish that a certain triplet is existing in more than one ontology at the same time.
This will be useful in the future if one of the ontologies is down or under maintenance, offering other choices to end-users to retrieve requested information.
Main Subjects
Information Technology and Computer Science
Topics
No. of Pages
127
Table of Contents
Table of contents.
Abstract.
Abstract in Arabic.
Chapter One : Introduction.
Chapter Two : Background and preliminaries.
Chapter Three : Literature survey and related works.
Chapter Four : Research methodology.
Chapter Five : Results and discussion.
Chapter Six : Conclusion and future work.
References.
American Psychological Association (APA)
Darwish, Rami Hilmi. (2018). Enhancing the ontologies matching in semantic web using artificial neural network. (Master's theses Theses and Dissertations Master). Princess Sumaya University for Technology, Jordan
https://search.emarefa.net/detail/BIM-795726
Modern Language Association (MLA)
Darwish, Rami Hilmi. Enhancing the ontologies matching in semantic web using artificial neural network. (Master's theses Theses and Dissertations Master). Princess Sumaya University for Technology. (2018).
https://search.emarefa.net/detail/BIM-795726
American Medical Association (AMA)
Darwish, Rami Hilmi. (2018). Enhancing the ontologies matching in semantic web using artificial neural network. (Master's theses Theses and Dissertations Master). Princess Sumaya University for Technology, Jordan
https://search.emarefa.net/detail/BIM-795726
Language
English
Data Type
Arab Theses
Record ID
BIM-795726
