A semantic framework for discovering casual relationships

The explosive growth of information at a mind-boggling scale has become an emerging phenomenon of our times. Discovering knowledge from a vast pool of resources is expected to remain a major challenge. In this respect, the extraction of semantic relations then becomes an important research area....

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Main Author:	Amaal Saleh, Hasan Al-Hashimy
Format:	Thesis
Language:	English
Published:	2015
Subjects:	ZA4050 Electronic information resources
Online Access:	http://ir.unimas.my/id/eprint/9287/1/Amaal%20Saleh%20Hasan%20Al-Hashimy%20ft.pdf
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id	my-unimas-ir.9287
record_format	uketd_dc
spelling	my-unimas-ir.92872023-03-24T00:13:53Z A semantic framework for discovering casual relationships 2015 Amaal Saleh, Hasan Al-Hashimy ZA4050 Electronic information resources The explosive growth of information at a mind-boggling scale has become an emerging phenomenon of our times. Discovering knowledge from a vast pool of resources is expected to remain a major challenge. In this respect, the extraction of semantic relations then becomes an important research area. While the extraction of ontological relations has been widely explored, the discovery of non-taxonomic relations is still a major bottleneck. Current approaches tend to predominantly employ syntactic approaches and rely largely on extensive manual efforts in the construction of linguistic resources. Our literature review has revealed major gaps in terms of the extraction of non-taxonomic relationships, particularly when it comes to implicit relationships. As a response to this problem, our research then explores a semantic approach for addressing the discovery of non-taxonomic relations such as causal relationships. Based on an empirical study of causality theory and related works, we have formulated a semantic approach for extracting causal patterns in text. The proposed framework incorporates a novel causality sense extraction method, “Purpose Based Word Sense Disambiguation”, together with a context-specific approach, “Graph based Semantics”, for uncovering causality structural patterns. Our approach has produced a set of causality features that is even able to highlight implicit causality patterns. We have employed benchmark data sets of SemEval 2007 and SemEval 2010 data sets together with standard linguistic resources such as WordNet, SemCore and XWNGloss in producing a series of intermediary linguistic resources as building blocks of the framework. A new qualitative measure for determining causal patterns has been formulated and used in conjunction with a gold standard for validating the significance of the findings. We have employed the C5.0 classifier to evaluate the effectiveness of the causality patterns as derived v from the framework. We have demonstrated via the realization of the framework, a purely semantic approach is possible without the need for extensive manual efforts. This research will serve as a key milestone and basis for ensuing discovery of non-taxonomic semantic relations such as causality. Universiti Malaysia Sarawak, (UNIMAS) 2015 Thesis http://ir.unimas.my/id/eprint/9287/ http://ir.unimas.my/id/eprint/9287/1/Amaal%20Saleh%20Hasan%20Al-Hashimy%20ft.pdf text en validuser phd doctoral Universiti Malaysia Sarawak, (UNIMAS) Faculty of Computer Science and Information Technology.
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English
topic	ZA4050 Electronic information resources
spellingShingle	ZA4050 Electronic information resources Amaal Saleh, Hasan Al-Hashimy A semantic framework for discovering casual relationships
description	The explosive growth of information at a mind-boggling scale has become an emerging phenomenon of our times. Discovering knowledge from a vast pool of resources is expected to remain a major challenge. In this respect, the extraction of semantic relations then becomes an important research area. While the extraction of ontological relations has been widely explored, the discovery of non-taxonomic relations is still a major bottleneck. Current approaches tend to predominantly employ syntactic approaches and rely largely on extensive manual efforts in the construction of linguistic resources. Our literature review has revealed major gaps in terms of the extraction of non-taxonomic relationships, particularly when it comes to implicit relationships. As a response to this problem, our research then explores a semantic approach for addressing the discovery of non-taxonomic relations such as causal relationships. Based on an empirical study of causality theory and related works, we have formulated a semantic approach for extracting causal patterns in text. The proposed framework incorporates a novel causality sense extraction method, “Purpose Based Word Sense Disambiguation”, together with a context-specific approach, “Graph based Semantics”, for uncovering causality structural patterns. Our approach has produced a set of causality features that is even able to highlight implicit causality patterns. We have employed benchmark data sets of SemEval 2007 and SemEval 2010 data sets together with standard linguistic resources such as WordNet, SemCore and XWNGloss in producing a series of intermediary linguistic resources as building blocks of the framework. A new qualitative measure for determining causal patterns has been formulated and used in conjunction with a gold standard for validating the significance of the findings. We have employed the C5.0 classifier to evaluate the effectiveness of the causality patterns as derived v from the framework. We have demonstrated via the realization of the framework, a purely semantic approach is possible without the need for extensive manual efforts. This research will serve as a key milestone and basis for ensuing discovery of non-taxonomic semantic relations such as causality.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Amaal Saleh, Hasan Al-Hashimy
author_facet	Amaal Saleh, Hasan Al-Hashimy
author_sort	Amaal Saleh, Hasan Al-Hashimy
title	A semantic framework for discovering casual relationships
title_short	A semantic framework for discovering casual relationships
title_full	A semantic framework for discovering casual relationships
title_fullStr	A semantic framework for discovering casual relationships
title_full_unstemmed	A semantic framework for discovering casual relationships
title_sort	semantic framework for discovering casual relationships
granting_institution	Universiti Malaysia Sarawak, (UNIMAS)
granting_department	Faculty of Computer Science and Information Technology.
publishDate	2015
url	http://ir.unimas.my/id/eprint/9287/1/Amaal%20Saleh%20Hasan%20Al-Hashimy%20ft.pdf
_version_	1783728041617260544

A semantic framework for discovering casual relationships

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