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Ontology Learning from the Arabic Text of the Qur’an: Concepts Identification and Hierarchical Relationships Extraction

Alrehaili, Sameer Mabrouk A. (2017) Ontology Learning from the Arabic Text of the Qur’an: Concepts Identification and Hierarchical Relationships Extraction. PhD thesis, University of Leeds.

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Abstract

Recent developments in ontology learning have highlighted the growing role ontologies play in linguistic and computational research areas such as language teaching and natural language processing. The ever-growing availability of annotations for the Qur’an text has made the acquisition of the ontological knowledge promising. However, the availability of resources and tools for Arabic ontology is not comparable with other languages. Manual ontology development is labour-intensive, time-consuming and it requires knowledge and skills of domain experts. This thesis aims to develop new methods for Ontology learning from the Arabic text of the Qur’an, including concepts identification and hierarchical relationships extraction. The thesis presents a methodology for reducing human intervention in building ontology from Classical Arabic Language of the Qur’an text. The set of concepts, which is a crucial step in ontology learning, was generated based on a set of patterns made of lexical and inflectional information. The concepts were identified based on adapted weighting schema that exploit a combination of knowledge to learn the relevance degree of a term. Statistical, domain-specific knowledge and internal information of Multi-Word Terms (MWTs) were combined to learn the relevance of generated terms. This methodology which represents the major contribution of the thesis was experimentally investigated using different terms generation methods. As a result, we provided the Arabic Qur’anic Terms (AQT) as a training resource for machine learning based term extraction. This thesis also introduces a new approach for hierarchical relations extraction from Arabic text of the Qur’an. A set of hierarchical relations occurring between identified concepts are extracted based on hybrid methods including head-modifier, set of markers for copula construct in Arabic text, referents. We also compared a number of ontology alignment methods for matching ontological bilingual Qur’anic resources. In addition, a multi-dimensional resource named Arabic Qur’anic Database (AQD) about the Qur’an is made for Arabic computational researchers, allowing regular expression query search over the included annotations. The search tool was successfully applied to find instances for a given complex rule made of different combined resources.

Item Type: Thesis (PhD)
Keywords: Ontology Learning, Concepts Identificaion, Hierarchical Relationships Extraction.
Academic Units: The University of Leeds > Faculty of Engineering (Leeds) > School of Computing (Leeds)
Identification Number/EthosID: uk.bl.ethos.741183
Depositing User: Sameer Mabrouk A. Alrehaili
Date Deposited: 14 May 2018 15:13
Last Modified: 25 Jul 2018 09:57
URI: http://etheses.whiterose.ac.uk/id/eprint/20309

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