Improved semantic graph-based plagiarism detection

Plagiarism detection occurs when the content of a text is copied without permission or citation. Nowadays, many text documents on the internet are easily copied and accessed. This study proposed improved methods to handle plagiarism. The proposed plagiarism detection methods are developed using grap...

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Main Author:	Osman Ahmed, Ahmed Hamza
Format:	Thesis
Language:	English
Published:	2013
Subjects:	PN Literature (General)
Online Access:	http://eprints.utm.my/id/eprint/33795/5/AhmedHamzaOsmanPFSKSM2013.pdf
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id	my-utm-ep.33795
record_format	uketd_dc
spelling	my-utm-ep.337952017-07-23T04:27:17Z Improved semantic graph-based plagiarism detection 2013-03 Osman Ahmed, Ahmed Hamza PN Literature (General) Plagiarism detection occurs when the content of a text is copied without permission or citation. Nowadays, many text documents on the internet are easily copied and accessed. This study proposed improved methods to handle plagiarism. The proposed plagiarism detection methods are developed using graph-based representation and semantic role labeling which are improved using fuzzy logic technique and chi-squared automatic interaction detection. The graph-based method does not only represent the content of a text document as a graph, but also captures the underlying semantic meaning in terms of the relationships among its concepts. Semantic role labeling is superior in generating semantic arguments for each sentence. This semantic role labeling plays an important part in plagiarism detection as it segments the role of concepts in documents to labels which are compared and used to detect plagiarism. Scoring for each argument generated by the fuzzy logic method to select important arguments is also another feature of this study. Chisquared Automatic Interaction Detection technique was applied to enforce the results obtained from the fuzzy logic and semantic role labeling by selecting important arguments from the sentences. It is concluded that not all arguments in the text are useful in the plagiarism detection process. Therefore, only the most important arguments were selected by the fuzzy logic and Chi-squared automatic interaction detection, and the results were used in the similarity calculation process. Experiments were tested on the PAN-PC-2009 for standard artificial simulation corpus and the Short Answers Questions (CS11) for human simulation corpus in plagiarism detection. The proposed methods detected many types of plagiarisms, such as copy paste plagiarism, rewording or synonym replacement, changing of word structure in the sentences, modifying the sentence from passive voice to active voice and vice-versa. Results from the experiments using the proposed methods in comparison to other palagiarism detection techniques (Fuzzy Semantic-Based String Similarity and Longest Common Subsequence) achieved better performance in terms of recall (93%), precision (90%) and f-measure (91%). 2013-03 Thesis http://eprints.utm.my/id/eprint/33795/ http://eprints.utm.my/id/eprint/33795/5/AhmedHamzaOsmanPFSKSM2013.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:69876?site_name=Restricted Repository phd doctoral Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	PN Literature (General)
spellingShingle	PN Literature (General) Osman Ahmed, Ahmed Hamza Improved semantic graph-based plagiarism detection
description	Plagiarism detection occurs when the content of a text is copied without permission or citation. Nowadays, many text documents on the internet are easily copied and accessed. This study proposed improved methods to handle plagiarism. The proposed plagiarism detection methods are developed using graph-based representation and semantic role labeling which are improved using fuzzy logic technique and chi-squared automatic interaction detection. The graph-based method does not only represent the content of a text document as a graph, but also captures the underlying semantic meaning in terms of the relationships among its concepts. Semantic role labeling is superior in generating semantic arguments for each sentence. This semantic role labeling plays an important part in plagiarism detection as it segments the role of concepts in documents to labels which are compared and used to detect plagiarism. Scoring for each argument generated by the fuzzy logic method to select important arguments is also another feature of this study. Chisquared Automatic Interaction Detection technique was applied to enforce the results obtained from the fuzzy logic and semantic role labeling by selecting important arguments from the sentences. It is concluded that not all arguments in the text are useful in the plagiarism detection process. Therefore, only the most important arguments were selected by the fuzzy logic and Chi-squared automatic interaction detection, and the results were used in the similarity calculation process. Experiments were tested on the PAN-PC-2009 for standard artificial simulation corpus and the Short Answers Questions (CS11) for human simulation corpus in plagiarism detection. The proposed methods detected many types of plagiarisms, such as copy paste plagiarism, rewording or synonym replacement, changing of word structure in the sentences, modifying the sentence from passive voice to active voice and vice-versa. Results from the experiments using the proposed methods in comparison to other palagiarism detection techniques (Fuzzy Semantic-Based String Similarity and Longest Common Subsequence) achieved better performance in terms of recall (93%), precision (90%) and f-measure (91%).
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Osman Ahmed, Ahmed Hamza
author_facet	Osman Ahmed, Ahmed Hamza
author_sort	Osman Ahmed, Ahmed Hamza
title	Improved semantic graph-based plagiarism detection
title_short	Improved semantic graph-based plagiarism detection
title_full	Improved semantic graph-based plagiarism detection
title_fullStr	Improved semantic graph-based plagiarism detection
title_full_unstemmed	Improved semantic graph-based plagiarism detection
title_sort	improved semantic graph-based plagiarism detection
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2013
url	http://eprints.utm.my/id/eprint/33795/5/AhmedHamzaOsmanPFSKSM2013.pdf
_version_	1747816187011530752

Improved semantic graph-based plagiarism detection

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