Ontology enrichment with causation relations

Ontology learning is considered a potential approach that can help to reduce the bottleneck of knowledge acquisition. However it suffers from a lack of standards to define concepts, besides the lack of fully automatic knowledge acquisition methods. In performing this learning process, the discovery...

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Main Authors:	Amaal Saleh Hassan, Al Hashimy, Narayanan, Kulathuramaiyer
Format:	Article
Language:	English
Published:	IEEE 2014
Subjects:	T Technology (General)
Online Access:	http://ir.unimas.my/16390/ http://ir.unimas.my/16390/ http://ir.unimas.my/16390/ http://ir.unimas.my/16390/1/Ontology%20Enrichment%20with%20Causation%20Relations%20%28abstract%29.pdf

id	unimas-16390
recordtype	eprints
spelling	unimas-163902017-05-23T07:42:01Z http://ir.unimas.my/16390/ Ontology enrichment with causation relations Amaal Saleh Hassan, Al Hashimy Narayanan, Kulathuramaiyer T Technology (General) Ontology learning is considered a potential approach that can help to reduce the bottleneck of knowledge acquisition. However it suffers from a lack of standards to define concepts, besides the lack of fully automatic knowledge acquisition methods. In performing this learning process, the discovery of non-taxonomic relationships has been identified as being the most difficult. This study is then an attempt to create an enhanced framework for discovering and classifying ontological relationships by using a machine learning strategy. We take into consideration the context of the input text in performing the classification of the semantic relations, in particular, causation relations. The proposed framework extracts initial semantic patterns for causation relation from the input samples, then filters these patterns using two novel algorithms, namely, the “Purpose Based Word Sense Disambiguation” which helps in determining the causation senses for input pair of words and the “Graph Based Semantics” which determines the existence of the causation relations in the sentence and to extract their cause-effect parts. The results show a good performance and the implemented framework cut off many steps of the usual process to produce the final results. IEEE 2014 Article PeerReviewed text en http://ir.unimas.my/16390/1/Ontology%20Enrichment%20with%20Causation%20Relations%20%28abstract%29.pdf Amaal Saleh Hassan, Al Hashimy and Narayanan, Kulathuramaiyer (2014) Ontology enrichment with causation relations. IEEE Conference on Systems, Process & Control (ICSPC), 2013. ISSN ISBN: 978-1-4799-2209-3 http://ieeexplore.ieee.org/document/6735129/ DOI: 10.1109/SPC.2013.6735129
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Sarawak
building	UNIMAS Institutional Repository
collection	Online Access
language	English
topic	T Technology (General)
spellingShingle	T Technology (General) Amaal Saleh Hassan, Al Hashimy Narayanan, Kulathuramaiyer Ontology enrichment with causation relations
description	Ontology learning is considered a potential approach that can help to reduce the bottleneck of knowledge acquisition. However it suffers from a lack of standards to define concepts, besides the lack of fully automatic knowledge acquisition methods. In performing this learning process, the discovery of non-taxonomic relationships has been identified as being the most difficult. This study is then an attempt to create an enhanced framework for discovering and classifying ontological relationships by using a machine learning strategy. We take into consideration the context of the input text in performing the classification of the semantic relations, in particular, causation relations. The proposed framework extracts initial semantic patterns for causation relation from the input samples, then filters these patterns using two novel algorithms, namely, the “Purpose Based Word Sense Disambiguation” which helps in determining the causation senses for input pair of words and the “Graph Based Semantics” which determines the existence of the causation relations in the sentence and to extract their cause-effect parts. The results show a good performance and the implemented framework cut off many steps of the usual process to produce the final results.
format	Article
author	Amaal Saleh Hassan, Al Hashimy Narayanan, Kulathuramaiyer
author_facet	Amaal Saleh Hassan, Al Hashimy Narayanan, Kulathuramaiyer
author_sort	Amaal Saleh Hassan, Al Hashimy
title	Ontology enrichment with causation relations
title_short	Ontology enrichment with causation relations
title_full	Ontology enrichment with causation relations
title_fullStr	Ontology enrichment with causation relations
title_full_unstemmed	Ontology enrichment with causation relations
title_sort	ontology enrichment with causation relations
publisher	IEEE
publishDate	2014
url	http://ir.unimas.my/16390/ http://ir.unimas.my/16390/ http://ir.unimas.my/16390/ http://ir.unimas.my/16390/1/Ontology%20Enrichment%20with%20Causation%20Relations%20%28abstract%29.pdf
first_indexed	2018-09-06T16:28:49Z
last_indexed	2018-09-06T16:28:49Z
_version_	1610876274648023040

Ontology enrichment with causation relations

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