Methods Inf Med 2021; 60(05/06): 162-170
DOI: 10.1055/s-0041-1735168
Original Article

Ontology Engineering for Gastric Dystemperament in Persian Medicine

Hassan Shojaee-Mend
1   Department of Basic Sciences, Faculty of Medicine, Gonabad University of Medical Sciences, Gonabad, Iran
2   Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
,
Haleh Ayatollahi
3   Health Management and Economics Research Center, Health Management Research Institute, Iran University of Medical Sciences, Tehran, Iran
,
Azam Abdolahadi
4   Department of Complementary Medicine, Research Institute for Islamic and Complementary Medicine, Iran University of Medical Sciences, Tehran, Iran
› Author Affiliations
Funding This work was funded and supported by Iran University of Medical Sciences, Tehran, Iran (IUMS/SHMIS_97–3-37–12671).

Abstract

Objective Developing an ontology can help collecting and sharing information in traditional medicine including Persian medicine in a well-defined format. The present study aimed to develop an ontology for gastric dystemperament in the Persian medicine.

Methods This was a mixed-methods study conducted in 2019. The first stage was related to providing an ontology requirements specification document. In the second stage, important terms, concepts, and their relationships were identified via literature review and expert panels. Then, the results derived from the second stage were refined and validated using the Delphi method in three rounds. Finally, in the fourth stage, the ontology was evaluated in terms of consistency and coherence.

Results In this study, 241 concepts related to different types of gastric dystemperament, diagnostic criteria, and treatments in the Persian medicine were identified through literature review and expert panels, and 12 new concepts were suggested during the Delphi study. In total, after performing three rounds of the Delphi study, 233 concepts were identified. Finally, an ontology was developed with 71 classes, and the results of the evaluation study revealed that the ontology was consistent and coherent.

Conclusion In this study, an ontology was created for gastric dystemperament in the Persian medicine. This ontology can be used for designing future systems, such as case-based reasoning and expert systems. Moreover, the use of other evaluation methods is suggested to construct a more complete and precise ontology.

Supplementary Material



Publication History

Received: 20 February 2021

Accepted: 12 July 2021

Article published online:
26 August 2021

© 2021. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 
  • References

  • 1 World Health Organization. WHO Traditional Medicine Strategy. 2014–2023. Geneva: World Health Organization; 2013
  • 2 Amjad S, Waheed T, Enriquez AMM, Aslam M, Syed A. An ontology based knowledge preservation model for traditional Unani medicines. In: Slezak D, Tan A-H, Peters JF, Schwabe L, eds. Paper presented at: Proceedings of the 2014 International Conference on Brain Informatics and Health; 2014 August 11–14; Warsaw, Poland: Springer; 2014: 278-289
  • 3 Emami M, Sadeghpour O, Zarshenas MM. Geriatric management in medieval Persian medicine. J Midlife Health 2013; 4 (04) 210-215
  • 4 Shojaee-Mend H, Ayatollahi H, Abdolahadi A. Developing a mobile-based disease ontology for traditional Persian medicine. Inform Med Unlocked 2020; 20: 100353
  • 5 Shojaee-Mend H, Ayatollahi H, Abdolahadi A. Development and evaluation of ontologies in traditional medicine: a review study. Methods Inf Med 2019; 58 (06) 194-204
  • 6 Blobel B. Case-based reasoning in intelligent health decision support systems. PHealth 2013. Paper presented at: Proceedings of the 10th International Conference on Wearable Micro and Nano Technologies for Personalized Health. IOS Press; 2013
  • 7 Dendani N, Khadir M, Guessoum S, Mokhtar B. Use a domain ontology to develop knowledge intensive CBR systems for fault diagnosis. Paper presented at: Proceedings of 2012 International Conference on Information Technology and e-Services (ICITeS). 24–26 March 2012, Sousse, Tunisia: IEEE. 1-6
  • 8 Amailef K, Lu J. Ontology-supported case-based reasoning approach for intelligent m-Government emergency response services. Decis Support Syst 2013; 55 (01) 79-97
  • 9 Holsapple CW, Joshi KD. A collaborative approach to ontology design. Commun ACM 2002; 44 (02) 42-47
  • 10 Hsu C-C, Sandford BA. The Delphi technique: making sense of consensus. Pract Assess, Res Eval 2007; 12 (10) 1-8
  • 11 Danial-Saad A, Kuflik T, Tamar Weiss PL, Schreuer N. Building an ontology for assistive technology using the Delphi method. Disabled Rehabil Assist Technol 2013; 8 (04) 275-286
  • 12 Heimonen J, Danielsson-Ojala R, Salakoski T, Lundgrén-Laine H, Salanterä S. Ontology development for patient education documents using a professional- and patient-oriented Delphi method. Comput Inform Nurs 2018; 36 (09) 448-457
  • 13 Kim Y-I, Kim M-C, Kang H-S. Research on ontology constructing by Delphi technique (with modeling micheogul tourist resort). Paper presented at: Proceedings of the CALSEC Conference; 2005 Society for e-Business Studies.
  • 14 Babaeian M, Borhani M, Hajiheidari MR. et al. Gastrointestinal system in the viewpoint of traditional Iranian medicine. J Islamic Iran Tradit Med 2012; 2 (04) 303-314
  • 15 Suárez-Figueroa MC, Gómez-Pérez A, Villazón-Terrazas B. eds. How to Write and Use the Ontology Requirements Specification Document. Paper presented at: OTM Confederated International Conference on the Move to Meaningful Internet Systems; 2009 Springer.
  • 16 Noy NF, McGuinness DL. Ontology development 101: A Guide to Creating Your First Ontology. Stanford Knowledge Systems Laboratory Technical Report KSL-01–05; 2001
  • 17 Kurilovas E, Juskeviciene A. Creation of web 2.0 tools ontology to improve learning. Comput Human Behav 2015; 51: 1380-1386
  • 18 Peroni S. A Simplified Agile Methodology for Ontology Development. OWL: Experiences and Directions—Reasoner Evaluation. Springer; 2016: 55-69
  • 19 El-Sappagh SH, El-Masri S, Elmogy M, Riad AM, Saddik B. An ontological case base engineering methodology for diabetes management. J Med Syst 2014; 38 (08) 67
  • 20 El-Sappagh S, Elmogy M. A fuzzy ontology modeling for case base knowledge in diabetes mellitus domain. Int J Eng Sci Technol 2017; 20 (03) 1025-1040
  • 21 Heras S, Botti V, Julián V. ArgCBROnto: A knowledge representation formalism for case-based argumentation. In: Chesñevar CI, Onaindia E, Ossowski S, Vouros G. eds. Agreement Technologies. (Lecture Notes in Computer Science; Vol. 8068). Berlin, Heidelberg: Springer; 2013
  • 22 Juarez JM, Salort J, Palma JT, Marin R. Case representation ontology for case retrieval systems in medical domains. Paper presented at: Proceedings of the 25th IASTED International Multiconference: Artificial Intelligence and Applications; 2007: 168-173
  • 23 Recio-García JA, Díaz-Agudo B, González-Calero PA. The COLIBRI Platform: Tools, Features and Working Examples. Successful case-based reasoning applications-2. Springer; 2014: 55-85
  • 24 Arzani M. Akbari's Medicine (Teb Akbari). Qom: Jalaleddin Publications; 2009
  • 25 Jahan N. Exir-e-Azam. Tehran: Institute for Islamic and Complementary Medicine; 2008
  • 26 Kermani N. Sharh-ol-Asbab val Alamat. Tehran: Research Institute for Islamic and Complementary Medicine Publication; 2008
  • 27 Ab Latif R, Dahlan A, Mulud ZA, Nor MZM. The Delphi technique as a method to obtain consensus in health care education research. Education in Medicine Journal. 2017; 9 (03) 89-102
  • 28 Schekotihin K, Rodler P, Schmid W, Horridge M, Tudorache T. eds. Test-Driven Ontology Development in Protégé. Paper presented at: Proceedings of the 9th International Conference on Biological Ontology (ICBO 2018), Corvallis, Oregon, USA; 2018
  • 29 Shojaee-Mend H, Ayatollahi H, Abdolahadi A. Case-base ontology for Persian medicine gastric dystemperament. 2021 . Accessed August 7, 2021 at: https://bioportal.bioontology.org/ontologies/CASE-BASE-ONTO
  • 30 Abburu S. A survey on ontology reasoners and comparison. Int J Comput Appl 2012; 57 (17) 33-39
  • 31 Achiepo OYM, N'Guessan BG, Brou KM. Similarity measure in the case-based reasoning systems for medical diagnostics in traditional medicine. International Journal of Computer Science Issues. 2015; 12 (02) 239
  • 32 Díaz-Agudo B, González-Calero PA. eds. An Architecture for Knowledge Intensive CBR Systems. Paper presented at: Proceedings of Advances in Case-Based Reasoning, 5th European Workshop, EWCBR 2000, Trento, Italy, September 6–9, 2000
  • 33 Gan M, Dou X, Jiang R. From ontology to semantic similarity: calculation of ontology-based semantic similarity. ScientificWorldJournal 2013; 2013: 793091
  • 34 Sánchez D, Batet M, Isern D, Valls A. Ontology-based semantic similarity: a new feature-based approach. Expert Syst Appl 2012; 39 (09) 7718-7728
  • 35 Forbes DE, Wongthongtham P, Terblanche C, Pakdeetrakulwong U. Ontology engineering. In: Forbes DE, Wongthongtham P, Terblanche C, Pakdeetrakulwong U. eds. Ontology Engineering Applications in Healthcare and Workforce Management Systems. Cham: Springer; 2018: 27-40
  • 36 Casellas N. Methodologies, Tools and Languages for Ontology Design. Legal Ontology Engineering. Netherland: Springer; 2011: 57-107
  • 37 Zhukova I, Kultsova M, Navrotsky M, Dvoryankin A. eds. Intelligent Support of Decision Making in Human Resource Management Using Case-Based Reasoning and Ontology. Paper presented at: 11th Joint Conference, JCKBSE 2014, Volgograd, Russia, September 17-20, 2014 Springer.
  • 38 Hajbagheri A, Parvizi S, Salsali M. Qualitative Research Methods. Tehran: Boshra; 2011
  • 39 McMillan SS, King M, Tully MP. How to use the nominal group and Delphi techniques. Int J Clin Pharm 2016; 38 (03) 655-662
  • 40 Brank J, Grobelnik M, Mladenic D. A survey of ontology evaluation techniques. In: Proceedings of the Conference on Data Mining and Data Warehouses 2005 October 5; Ljubljana, Slovenia: Citeseer; 2005: 166-170
  • 41 Fathian Dastgerdi A. Ontology evaluation: consideration of criteria, approaches and layers. Iran J Inf Process Manage 2012; 27 (02) 533-559