Content-Based Scientific Figure Plagiarism Detection Using Semantic Mapping

  • Taiseer Abdalla Elfadil EisaEmail author
  • Naomie Salim
  • Abdelzahir Abdelmaboud
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1073)


Plagiarism is to steal others’ work using their words directly or indirectly without a credit citation. Copying others’ ideas is another type of plagiarism that may occur in many areas but the most serious one is the academic plagiarism. Academic misconduct forms high-profile plagiarism cases at universities. Therefore, technical solutions are strictly demanded for automatic idea plagiarism detection. Detection of figure plagiarism is a challenge field of research because not only the text analytics but also graphic features are analyzed. This paper investigates the issue of idea and figure plagiarism and proposes a detection method which copes with text and structure change. The procedure depends on finding similar semantic meanings between figures by applying image processing and semantic mapping techniques.


Plagiarism detection Figure plagiarism detection Semantic meanings Scientific publications 


  1. 1.
    Alzahrani, S.M., Salim, N., Abraham, A.: Understanding plagiarism linguistic patterns, textual features, and detection methods. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 42(2), 133–149 (2012)Google Scholar
  2. 2.
    Sergey, B., et al.: Embedding plagiarism detection mechanisms into learning management systems. In: Scholarly Ethics and Publishing: Breakthroughs in Research and Practice, A. Information Resources Management, Hershey, PA, USA, pp. 216–231. IGI Global (2019)Google Scholar
  3. 3.
    Bouville, M.: Plagiarism: words and ideas. Sci. Eng. Ethics 14(3), 311–322 (2008)CrossRefGoogle Scholar
  4. 4.
    Habibzadeh, F., Shashok, K.: Plagiarism in scientific writing: words or ideas? Croat. Med. J. 52(4), 576–577 (2011)CrossRefGoogle Scholar
  5. 5.
    Vani, K., Gupta, D.: Unmasking text plagiarism using syntactic-semantic based natural language processing techniques: comparisons, analysis and challenges. Inf. Process. Manag. 54(3), 408–432 (2018)CrossRefGoogle Scholar
  6. 6.
    Agarwal, B., et al.: A deep network model for paraphrase detection in short text messages. Inf. Process. Manag. 54(6), 922–937 (2018)CrossRefGoogle Scholar
  7. 7.
    Meuschke, N., et al.: HyPlag: a hybrid approach to academic plagiarism detection. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, Ann Arbor, MI, USA, pp. 1321–1324. ACM (2018)Google Scholar
  8. 8.
    Meuschke, N., et al.: An adaptive image-based plagiarism detection approach. In: Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries, Fort Worth, Texas, USA, pp. 131–140. ACM (2018)Google Scholar
  9. 9.
    Osman, A.H., et al.: An improved plagiarism detection scheme based on semantic role labeling. Appl. Soft Comput. 12(5), 1493–1502 (2012)CrossRefGoogle Scholar
  10. 10.
    Alzahrani, S., et al.: iPlag: intelligent plagiarism reasoner in scientific publications. In: 2011 World Congress on Information and Communication Technologies (WICT) (2011)Google Scholar
  11. 11.
    Deepika, J., et al.: A knowledge based approach to detection of idea plagiarism in online research publications. Int. J. Internet Distrib. Comput. Syst. 1(2), 51–61 (2011)Google Scholar
  12. 12.
    Shenoy, M.K., Shet, K., Acharya, U.D.: Semantic plagiarism detection system using ontology mapping. Adv. Comput. 3(3), 59–62 (2012)Google Scholar
  13. 13.
    Foudeh, P., Salim, N.: A holistic approach to duplicate publication and plagiarism detection using probabilistic ontologies. Springer, Heidelberg, pp. 566–574 (2012)Google Scholar
  14. 14.
    Al-Dabbagh, M.M., et al.: Intelligent bar chart plagiarism detection in documents. Sci. World J. 2014, 1–11 (2014)CrossRefGoogle Scholar
  15. 15.
    Rabiu, I., Salim, N.: Textual and structural approaches to detecting figure plagiarism in scientific publications. J. Theor. Appl. Inf. Technol. 70(2), 356–371 (2014)Google Scholar
  16. 16.
    Arrish, S., et al.: Shape-based plagiarism detection for flowchart figures in texts. Int. J. Comput. Sci. Inf. Technol. (IJCSIT) 6(1), 113–124 (2014)Google Scholar
  17. 17.
    Mason, P.R.: Plagiarism in Scientific Publications Editorial Article (2009)Google Scholar
  18. 18.
    Habibzadeh, F., Shashok, K.: Plagiarism in scientific writing: words or ideas? PMC3160704 (2011)Google Scholar
  19. 19.
    Al-Dabbagh, M.M., Salim, N., Rehman, A., et al.: Intelligent bar chart plagiarism detection in documents. Sci. World J. 2014, 612787 (2014)Google Scholar
  20. 20.
    Mitra, P., Noy, N., Jaiswal, A.: OMEN: a probabilistic ontology mapping tool. In: Gil, Y., et al. (eds.) The Semantic Web – ISWC 2005, pp. 537–547. Springer, Heidelberg (2005)Google Scholar
  21. 21.
    Wang, P., Xu, B.: Debugging ontology mappings: a static approach. Comput. Inform. 27, 21–36 (2008)Google Scholar
  22. 22.
    Shahri, S., Jamil, H.: An extendable meta-learning algorithm for ontology mapping. In: Andreasen, T., et al. (eds.) Flexible Query Answering Systems, pp. 418–430. Springer, Heidelberg (2009)Google Scholar
  23. 23.
    Albagli, S., Ben-Eliyahu-Zohary, R., Shimony, S.E.: Markov network based ontology matching. J. Comput. Syst. Sci. 78(1), 105–118 (2012)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Rubiolo, M., et al.: Knowledge discovery through ontology matching: an approach based on an Artificial Neural Network model. Inf. Sci. 194, 107–119 (2012)CrossRefGoogle Scholar
  25. 25.
    Kalfoglou, Y., Schorlemmer, M.: IF-Map: an ontology-mapping method based on information-flow theory. In: Spaccapietra, S., March, S., Aberer, K. (eds.) Journal on Data Semantics I, pp. 98–127. Springer, Heidelberg (2003)Google Scholar
  26. 26.
    Doussot, D., et al.: Using fuzzy conceptual graphs to map ontologies. In: Meersman, R., Tari, Z. (eds.) On the Move to Meaningful Internet Systems 2006: CoopIS, DOA, GADA, and ODBASE, pp. 891–900. Springer, Heidelberg (2006)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Taiseer Abdalla Elfadil Eisa
    • 1
    Email author
  • Naomie Salim
    • 2
  • Abdelzahir Abdelmaboud
    • 3
  1. 1.College of Science and Arts - Girls SectionKing Khalid UniversityMahayilSaudi Arabia
  2. 2.Faculty of Computer Science and Information SystemsUniversiti Teknologi MalaysiaSkudaiMalaysia
  3. 3.College of Science and Arts KingKhalid UniversityMahayilSaudi Arabia

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