MOTunAr Ontology: Creation and Axioms Impacts

Author
Degachi Haifa, Yengui Ameni, and Neji Mahmoud
Keywords
Axioms; Criteria; Evaluation; MOTunAr; Ontology.
Abstract
Due to its potential for supporting heterogeneous and various data, ontologies are used in serval domains, notably the archaeological one. In this paper, we want to present a multimedia ontology that designs the different entities involved in the Tunisian archaeological field. On another hand, the task of creating ontology is error-prone. The quality of ontology should be sequentially evaluated based on various criteria (e.g., coherence, consciences, interoperability, etc.). Axioms present the guarantor to satisfy high quality for a developed ontology. Therefore, we detail in this work a hybrid approach that guarantees the quality of the generated ontology. This approach combines the corrective method that defines the positive axioms, and the constructive method that defines all relevant axioms based on the elimination of model and integrity constraints. The generated ontology is evaluated with the Pellet reasoner and OOPS! Online service.
References
[1] B. Ben Mahria, I. Chaker, and A. Zahi, “A novel approach for lesarning ontology from relational database : from the construction to the evaluation,” J. Big Data, 2021, doi: 10.1186/s40537-021-00412-2.
[2] D. Haifa, Y. Ameni, and N. Mahmoud, “Accomplishment of multimedia ontology for the tunisian archaeology field,” in Advances in Intelligent Systems and Computing, vol. 1184, Springer Science and Business Media Deutschland GmbH, 2021, pp. 64–72.
[3] D. Haifa, Y. Ameni, and N. Mahmoud, “Multimedia Ontology of the Tunisian Archaeology Field,” in Advances in Intelligent Systems and Computing, Jul. 2020, vol. 1105 AISC, pp. 321–330, doi: 10.1007/978-3-030-36674-2_33.
[4] D. Haifa, Y. Ameni, and N. Mahmoud, “Towards a Multimedia Ontology for Tunisian Archaeology Field,” Dec. 2019, doi: 10.1109/ICTA49490.2019.9144900.
[5] S. Belabbes, S. Benferhat, and J. Chomicki, “Elect : Une méthode de gestion des incohérences dans des ontologies légères partiellement préordonnées *,” Jul. 2019, Accessed: Oct. 01, 2021. [Online]. Available: https://hal.archives-ouvertes.fr/hal-02301980.
[6] S. Salminawati, “ONTOLOGICAL BASIS OF SCIENCE CLASSIFICATION (Study on the Philosophy of Islamic Education),” Edukasi Islam. J. Pendidik. Islam, vol. 9, no. 02, pp. 683–700, Aug. 2020, doi: 10.30868/EI.V9I02.1581.
[7] M. Ben Salah, A. Yengui, M. Neji, and N. Mahmoud, “Construction of OTunAr : ontology of Tunisian archeology,” 2017. Accessed: Mar. 02, 2021. [Online]. Available: https://www.researchgate.net/publication/340922998.
[8] S. Gherbi and M. T. Khadir, “ONTMAT1: Ontology matching using a reasoner and property restriction,” Int. J. Web Eng. Technol., vol. 15, no. 2, pp. 119–139, 2020, doi: 10.1504/IJWET.2020.109728.
[9] A. Mathews, S. Chen, M. T. Bigham, and K. Mansel, “Oops: rapid Deterioration of the Transport Patient Admitted to the General Care Floor,” Pediatrics, vol. 141, no. 1 MeetingAbstract, pp. 728–728, Jan. 2018, doi: 10.1542/PEDS.141.1_MEETINGABSTRACT.728.
[10] L. Olsina, “ThingFO v1.2’s Terms, Properties, Relationships and Axioms — Foundational Ontology for Things,” Jul. 2021, Accessed: Jan. 30, 2022. [Online]. Available: http://arxiv.org/abs/2107.09129.
[11] C. Roche and M. Papadopoulou, “Terminologie et ontologie pour les humanités numériques : le cas des vêtements de la Grèce antiqueTerminology and Ontology for Digital Humanities: The Case of Ancient Greek Dress,” Humanit. numériques, no. 2, Jun. 2020, doi: 10.4000/revuehn.462.
[12] M. Rasmussen, M. Lefrançois, M. Bonduel, C. Hviid, and J. Karlshøj, “OPM: An ontology for describing properties that evolve over time,” Jun. 2018, Accessed: Oct. 01, 2021. [Online]. Available: https://hal.archives-ouvertes.fr/hal-01885248.
[13] H. J. Pandit, K. Fatema, D. O’Sullivan, and D. Lewis, GDPRtEXT – GDPR as a Linked Data Resource, vol. 10843 LNCS. 2018.
[14] M. Florrence, “MLGrafViz: Multilingual ontology visualization plug-in for protégé,” Comput. Sci. Inf. Technol., vol. 2, no. 1, pp. 43–48, Mar. 2021, doi: 10.11591/CSIT.V2I1.P43-48.
[15] F. Z. Smaili, X. Gao, and R. Hoehndorf, “Formal axioms in biomedical ontologies improve analysis and interpretation of associated data,” 2018, doi: 10.1093/bioinformatics/xxxxxx.
[16] F. Freitas and I. Varzinczak, “Cardinality Restrictions Within Description Logic Connection Calculi,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11092 LNCS, pp. 65–80, Sep. 2018, doi: 10.1007/978-3-319-99906-7_5.
[17] F. Song and E. De La Clergerie, “Clustering-based Automatic Construction of Legal Entity Knowledge Base from Contracts,” Proc. – 2020 IEEE Int. Conf. Big Data, Big Data 2020, pp. 2149–2152, Dec. 2020, doi: 10.1109/BIGDATA50022.2020.9378166.

Received :18February2022
Accepted : 25June2022
Published :29June 2022
DOI: 10.30726/esij/v9.i2.2022.92005

Download “MOTunAr-Ontology-Creation-and-Axioms-Impacts.pdf” MOTunAr-Ontology-Creation-and-Axioms-Impacts.pdf – Downloaded 15 times – 651 KB