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Weighted semantic plagiarism detection approach based on AHP decision model.

SeyyedMohammad JavadiMoghaddam1, Fatemeh Roosta2, Asadolla Noroozi3

  • 1Department of Computer Engineering, Bozorgmehr University of Qaenat, Qaen, Iran.

Accountability in Research
|April 1, 2021
PubMed
Summary

This study introduces a novel method for detecting academic plagiarism by analyzing key terms and their positions within documents. This approach enhances detection accuracy and significantly reduces processing time compared to existing systems.

Keywords:
AHP modelText similarityWordNetplagiarism detectionsemantic plagiarism

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Area of Science:

  • Computer Science
  • Information Science

Background:

  • Academic plagiarism is a growing concern, with sophisticated methods used to evade detection systems.
  • Existing plagiarism detection algorithms often struggle with semantic variations and inverted word order, and may not optimize for time complexity.

Purpose of the Study:

  • To propose an effective method for detecting structural and semantic plagiarism.
  • To reduce the time complexity of plagiarism detection by analyzing only essential parts of documents.
  • To improve the accuracy of plagiarism detection, even when word order is altered.

Main Methods:

  • Utilizing a subset of document content for similarity calculation, rather than the entire text.
  • Employing a set of significant terms and their combinations to enhance detection capabilities.
  • Assigning differential weights to words based on their location within document sections.
  • Implementing an Analytical Hierarchy Process (AHP) model for weighted similarity calculation.

Main Results:

  • The proposed approach demonstrates superior ability in detecting semantic academic plagiarism.
  • A significant reduction in runtime was observed compared to existing plagiarism detection methods.
  • The method effectively handles variations in word order and synonym usage.

Conclusions:

  • The developed method offers an efficient and accurate solution for academic plagiarism detection.
  • By focusing on key terms and positional weighting, the system overcomes limitations of traditional approaches.
  • This research contributes to more robust and time-efficient academic integrity tools.