Concept And Relation Extraction Framework For Ontology Learning

Extracting valuable knowledge and representing it in a machine-understandable form is considered one of the main challenges of semantic web and knowledge engineering fields. The explosive growth of textual data is coupled with the increasing demand for ontologies. Ontology Learning (OL) from text is...

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Main Author:	Al-Aswadi, Fatima Nadeem Salem
Format:	Thesis
Language:	English
Published:	2023
Subjects:	QA75.5-76.95 Electronic computers Computer science
Online Access:	http://eprints.usm.my/60149/1/FATIMA%20NADEEM%20SALEM%20AL-ASWADI%20-%20TESIS%20cut.pdf
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id	my-usm-ep.60149
record_format	uketd_dc
spelling	my-usm-ep.601492024-03-12T06:36:43Z Concept And Relation Extraction Framework For Ontology Learning 2023-08 Al-Aswadi, Fatima Nadeem Salem QA75.5-76.95 Electronic computers. Computer science Extracting valuable knowledge and representing it in a machine-understandable form is considered one of the main challenges of semantic web and knowledge engineering fields. The explosive growth of textual data is coupled with the increasing demand for ontologies. Ontology Learning (OL) from text is a process that aims to automatically or semi-automatically extract and represent the knowledge from text into the machine-understandable form. Ontology is a core scheme representing knowledge as a set of concepts and their relationships within a domain. Extracting the concepts and their relations is the backbone for an OL system. The existing OL systems have many limitations and drawbacks, such as not efficient for extracting relevant concepts especially, for large-length dataset; depending on a large amount of predefined patterns to extract relations, and extracting very few types of relations. In this thesis, a framework called Concept and Relation Extraction Framework (CREF) is proposed. It consists of four main stages: enhancing pre-processing method, developing methodology for the concept extraction task, proposing a new text representation approach for relations, and improving relation extraction method. The first stage involves proposing a new Concept Extraction stopwords (CE-stopwords) for scientific publications while the second stage involves introducing a new Domain Time Relevance (DTR) metric and proposing a Developed Concept Extraction Method based on DTR called (DTR-DCEM). 2023-08 Thesis http://eprints.usm.my/60149/ http://eprints.usm.my/60149/1/FATIMA%20NADEEM%20SALEM%20AL-ASWADI%20-%20TESIS%20cut.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Komputer
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA75.5-76.95 Electronic computers Computer science
spellingShingle	QA75.5-76.95 Electronic computers Computer science Al-Aswadi, Fatima Nadeem Salem Concept And Relation Extraction Framework For Ontology Learning
description	Extracting valuable knowledge and representing it in a machine-understandable form is considered one of the main challenges of semantic web and knowledge engineering fields. The explosive growth of textual data is coupled with the increasing demand for ontologies. Ontology Learning (OL) from text is a process that aims to automatically or semi-automatically extract and represent the knowledge from text into the machine-understandable form. Ontology is a core scheme representing knowledge as a set of concepts and their relationships within a domain. Extracting the concepts and their relations is the backbone for an OL system. The existing OL systems have many limitations and drawbacks, such as not efficient for extracting relevant concepts especially, for large-length dataset; depending on a large amount of predefined patterns to extract relations, and extracting very few types of relations. In this thesis, a framework called Concept and Relation Extraction Framework (CREF) is proposed. It consists of four main stages: enhancing pre-processing method, developing methodology for the concept extraction task, proposing a new text representation approach for relations, and improving relation extraction method. The first stage involves proposing a new Concept Extraction stopwords (CE-stopwords) for scientific publications while the second stage involves introducing a new Domain Time Relevance (DTR) metric and proposing a Developed Concept Extraction Method based on DTR called (DTR-DCEM).
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Al-Aswadi, Fatima Nadeem Salem
author_facet	Al-Aswadi, Fatima Nadeem Salem
author_sort	Al-Aswadi, Fatima Nadeem Salem
title	Concept And Relation Extraction Framework For Ontology Learning
title_short	Concept And Relation Extraction Framework For Ontology Learning
title_full	Concept And Relation Extraction Framework For Ontology Learning
title_fullStr	Concept And Relation Extraction Framework For Ontology Learning
title_full_unstemmed	Concept And Relation Extraction Framework For Ontology Learning
title_sort	concept and relation extraction framework for ontology learning
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Komputer
publishDate	2023
url	http://eprints.usm.my/60149/1/FATIMA%20NADEEM%20SALEM%20AL-ASWADI%20-%20TESIS%20cut.pdf
_version_	1794024096015581184

Concept And Relation Extraction Framework For Ontology Learning

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