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Survey Of Traditional And Semantic Plagiarism Detection Algorithms

استعراض خوارزميات كشف الانتحال التقليدية و الدلالية

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 Publication date 2016
and research's language is العربية
 Created by Shamra Editor




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In this paper we review and list, the advantages and limitations of the significant effective techniques employed or developed in text plagiarism detection. It was found that many of the proposed methods for plagiarism detection have a weakness points and do not detect some types of plagiarized operations. This paper show a survey about plagiarism detection including several important subjects in plagiarism detection, which is plagiarism definition, plagiarism prevention and detection, plagiarism detection systems, plagiarism detection processes and some of the current plagiarism detection techniques. This paper compares between different plagiarism detection algorithms, and shows the points of weakness, and points of efficiency, and describe the power of semantic plagiarism detection methods, and shows its efficiency in detect plagiarism cases that another plagiarism detection algorithms don’t able to detect these cases, that semantic plagiarism detection methods are developed to get rid of traditional weakness points for all plagiarism detection methods have.


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Research summary
تستعرض هذه الورقة العلمية تقنيات كشف الانتحال النصي، مع التركيز على الخوارزميات التقليدية والدلالية. تتناول الورقة تعريف الانتحال، وطرق الوقاية منه، وأنظمة الكشف عنه، بالإضافة إلى العمليات والتقنيات المستخدمة حاليًا. تقارن الورقة بين الخوارزميات المختلفة، وتوضح نقاط الضعف والقوة لكل منها، مع التركيز على فعالية الخوارزميات الدلالية في الكشف عن حالات الانتحال التي قد لا تتمكن الخوارزميات التقليدية من اكتشافها. تتناول الورقة أيضًا أنظمة الكشف عن الانتحال عبر الإنترنت والأنظمة المستقلة، وتناقش كيفية تقليل الانتحال من خلال الوقاية والكشف اليدوي والمساعد بالحاسوب. كما تقدم الورقة مقارنة شاملة بين الخوارزميات التقليدية والدلالية، وتوضح أن الخوارزميات الدلالية هي الأكثر كفاءة لكنها معقدة أكثر من الخوارزميات التقليدية بسبب استخدامها لمصادر الويب الدلالية.
Critical review
دراسة نقدية: تقدم هذه الورقة نظرة شاملة ومفصلة حول تقنيات كشف الانتحال، وتسلط الضوء على نقاط القوة والضعف في كل خوارزمية. ومع ذلك، يمكن القول أن الورقة تفتقر إلى تقديم أمثلة عملية أو دراسات حالة توضح كيفية تطبيق هذه الخوارزميات في بيئات حقيقية. كما أن التركيز الكبير على الخوارزميات الدلالية قد يجعل القارئ يشعر بأن الخوارزميات التقليدية ليست فعالة بما فيه الكفاية، على الرغم من أنها قد تكون كافية في بعض الحالات. بالإضافة إلى ذلك، يمكن أن تكون الورقة أكثر فائدة إذا تضمنت توصيات محددة حول كيفية تحسين الخوارزميات الحالية أو دمجها لتحقيق أفضل النتائج.
Questions related to the research
  1. ما هي الأنواع المختلفة للانتحال التي تم ذكرها في الورقة؟

    تشمل الأنواع المختلفة للانتحال التي تم ذكرها في الورقة: النسخ واللصق، انتحال الفقرات، انتحال الأفكار، والانتحال عبر اللغات من خلال الترجمة.

  2. ما هي الأنظمة المستخدمة في الكشف عن الانتحال عبر الإنترنت؟

    تشمل الأنظمة المستخدمة في الكشف عن الانتحال عبر الإنترنت: Turnitin وSafeAssign، حيث يستخدم كل منهما قواعد بيانات ضخمة من الإنترنت وأعمال الطلاب السابقة للمقارنة مع الوثيقة المشكوك فيها.

  3. ما هي نقاط الضعف الرئيسية في خوارزميات كشف الانتحال التقليدية؟

    تشمل نقاط الضعف الرئيسية في خوارزميات كشف الانتحال التقليدية: التأثر الشديد بإعادة ترتيب الكلمات واستبدال المرادفات، وصعوبة تحديد الطول الأمثل للسلاسل النصية للمطابقة، والغموض في اللغة الطبيعية الذي يؤدي إلى تمثيل النص بأكثر من شجرة واحدة.

  4. ما هي المزايا الرئيسية للخوارزميات الدلالية في كشف الانتحال؟

    المزايا الرئيسية للخوارزميات الدلالية في كشف الانتحال تشمل قدرتها على اكتشاف حالات الانتحال التي لا تستطيع الخوارزميات التقليدية اكتشافها، وذلك من خلال استخدام القواميس الدلالية واللغات الدلالية للويب لتحليل النصوص والكشف عن التشابهات الدلالية.


References used
J. J. G. Adeva, et al., "Applying plagiarism detection to engineering education," 2006, pp. 722-731
C. Lyon, et al., "Plagiarism is easy, but also easy to detect," Plagiary: CrossDisciplinary Studies in Plagiarism, Fabrication, and Falsification, vol. 1, 2006
L. Chao, L., et al., “GPLAG: detection of software plagiarism by program dependence graph analysis,” the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. 2006, ACM: Philadelphia, PA, USA
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