Abstract
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
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)
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)
Bouville, M.: Plagiarism: words and ideas. Sci. Eng. Ethics 14(3), 311–322 (2008)
Habibzadeh, F., Shashok, K.: Plagiarism in scientific writing: words or ideas? Croat. Med. J. 52(4), 576–577 (2011)
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)
Agarwal, B., et al.: A deep network model for paraphrase detection in short text messages. Inf. Process. Manag. 54(6), 922–937 (2018)
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)
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)
Osman, A.H., et al.: An improved plagiarism detection scheme based on semantic role labeling. Appl. Soft Comput. 12(5), 1493–1502 (2012)
Alzahrani, S., et al.: iPlag: intelligent plagiarism reasoner in scientific publications. In: 2011 World Congress on Information and Communication Technologies (WICT) (2011)
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)
Shenoy, M.K., Shet, K., Acharya, U.D.: Semantic plagiarism detection system using ontology mapping. Adv. Comput. 3(3), 59–62 (2012)
Foudeh, P., Salim, N.: A holistic approach to duplicate publication and plagiarism detection using probabilistic ontologies. Springer, Heidelberg, pp. 566–574 (2012)
Al-Dabbagh, M.M., et al.: Intelligent bar chart plagiarism detection in documents. Sci. World J. 2014, 1–11 (2014)
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)
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)
Mason, P.R.: Plagiarism in Scientific Publications Editorial Article (2009)
Habibzadeh, F., Shashok, K.: Plagiarism in scientific writing: words or ideas? PMC3160704 (2011)
Al-Dabbagh, M.M., Salim, N., Rehman, A., et al.: Intelligent bar chart plagiarism detection in documents. Sci. World J. 2014, 612787 (2014)
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)
Wang, P., Xu, B.: Debugging ontology mappings: a static approach. Comput. Inform. 27, 21–36 (2008)
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)
Albagli, S., Ben-Eliyahu-Zohary, R., Shimony, S.E.: Markov network based ontology matching. J. Comput. Syst. Sci. 78(1), 105–118 (2012)
Rubiolo, M., et al.: Knowledge discovery through ontology matching: an approach based on an Artificial Neural Network model. Inf. Sci. 194, 107–119 (2012)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Eisa, T.A.E., Salim, N., Abdelmaboud, A. (2020). Content-Based Scientific Figure Plagiarism Detection Using Semantic Mapping. In: Saeed, F., Mohammed, F., Gazem, N. (eds) Emerging Trends in Intelligent Computing and Informatics. IRICT 2019. Advances in Intelligent Systems and Computing, vol 1073. Springer, Cham. https://doi.org/10.1007/978-3-030-33582-3_40
Download citation
DOI: https://doi.org/10.1007/978-3-030-33582-3_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33581-6
Online ISBN: 978-3-030-33582-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)