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An Ontological Case Base Engineering Methodology for Diabetes Management

  • Systems-Level Quality Improvement
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Abstract

Ontology engineering covers issues related to ontology development and use. In Case Based Reasoning (CBR) system, ontology plays two main roles; the first as case base and the second as domain ontology. However, the ontology engineering literature does not provide adequate guidance on how to build, evaluate, and maintain ontologies. This paper proposes an ontology engineering methodology to generate case bases in the medical domain. It mainly focuses on the research of case representation in the form of ontology to support the case semantic retrieval and enhance all knowledge intensive CBR processes. A case study on diabetes diagnosis case base will be provided to evaluate the proposed methodology.

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References

  1. M. Sicilia (2014) Handbook of Metadata, Semantics and Ontologies, World Scientific Publishing Company.

  2. F. Noy, D. McGuinness (2001) Ontology development 101: A guide to creating your first ontology, Technical Report SMI-2001-0880, Stanford University School of Medicine

  3. Casellas, N., Methodologies. Tools and Languages for Ontology Design, Legal Ontology Engineering 3:57–107, 2011.

    Article  Google Scholar 

  4. Lenat, D., and Guha, R., Building large knowledge-based systems: Representation and inference in the CYC project. Artificial Intelligence 61(1):41–52, 1993.

    Article  Google Scholar 

  5. Uschold, M., and King, M., Towards a methodology for building ontologies. Artificial Intelligence Applications Institute, University of Edinburgh, pp. 15–30, 1995.

    Google Scholar 

  6. M. Fernández et al. (1997) METHONTOLOGY: From Ontological Art Towards Ontological Engineering, Symposium on Ontological Engineering of AAAI, 33–40

  7. Y. Sure, R. Studer (2004) On-To-Knowledge Methodology (OTKM), Handbook on Ontologies (part 2), pp117–132

  8. Chikofsky, E., Reverse Engineering and design recovery: A taxonomy. IEEE Software Magazine 7(1):13–17, 1990. doi:10.1109/52.43044.

    Article  Google Scholar 

  9. A. Gómez-Pérez, M. Rojas, (1999) Ontological Reengineering and Reuse, Proceedings of 11th European Knowledge Acquisition Workshop (EKAW), 1621: 139–156

  10. D. Calvanese, M. Lenzerini (2001) A Framework for Ontology Integration, Proceedings of Int. Semantic Web Working Symposium (SWWS), pp 663–667

  11. Recio-García, J., et al., jcolibri2: A framework for building Case-based reasoning systems. Science of Computer Programming 79:126–145, 2014.

    Article  Google Scholar 

  12. Dufour-Lussier, V., et al., Improving Case Retrieval by Enrichment of the Domain Ontology. Case-Based Reasoning Research and Development 6880:62–76, 2011.

    Article  Google Scholar 

  13. AbouAssali, A., et al., Heterogeneity in Ontological CBR Systems. Successful Case-based Reasoning Applications I, Studies in Computational Intelligence 305:97–116, 2010.

    Google Scholar 

  14. Subirats, L., and Ceccaroni, L., An Ontology for Computer-Based Decision Support in Rehabilitation. Advances in Artificial Intelligence 7094:549–559, 2011.

    Google Scholar 

  15. Disease Ontology, http://disease-ontology.org. Accessed April 2014.

  16. Ontology of Glucose Metabolism Disorder, http://rgd.mcw.edu/rgdweb/ontology/view.html? acc_id = RDO:0001421. Accessed April 2014.

  17. El-Sappagh, S., Elmogy, M., El-Masri, S., and Riad, A., A Diabetes Diagnostic Domain Ontology for CBR System from the Conceptual Model of SNOMED CT. Proceeding of the second IEEE Int. Con. on Engineering and Technology (ICET 2014), Cairo, 2014. Accepted.

    Google Scholar 

  18. Jaya, A., A Standard Methodology for the Construction of Symptoms Ontology for Diabetes Diagnosis. International Journal of Computer Applications 14(1):47–51, 2011.

    Article  Google Scholar 

  19. R. Subhashini, J. Akilandeswari (2011) A survey on ontology construction methodologies, Int. J. of Enterprise Computing and Business Systems, 1(1), www.ijecbs.com

  20. Gawich, M., Badr, A., Hegazy, A., and Ismail, H., A Methodology for Ontology Building. International Journal of Computer Applications 56(2):0975–8887, 2012.

    Article  Google Scholar 

  21. M. Gr¨uninger, M. Fox (1995) Methodology for the design and evaluation of ontologies, International Joint Conference on Artificial Intelligence (IJCAI95), Workshop on Basic Ontological Issues in Knowledge Sharing

  22. J. Correa (1996) Building and Reusing Ontologies for Electrical Network Applications, Proceeding of the 12th European Conference on Artificial Intelligence (ECAI96), 298–302

  23. Valente, A., Building and (Re)Using an Ontology of Air Campaign Planning. IEEE Intelligent Systems and their Applications 14(1):27–36, 1999.

    Article  Google Scholar 

  24. K. Kerremans, R. Temmerman, J. Tummers (2003) Representing multilingual and culture specific knowledge in a vat regulatory ontology: Support from the termontography methodology, Proceeding of the 1st International Workshop on Regulatory Ontologies and the Modeling of Complaint Regulations, 2889:662–674

  25. Jarrar, M., and Meersman, R., Ontology Engineering – The DOGMA Approach. Advances in Web Semantics I 4891:7–34, 2009.

    Article  Google Scholar 

  26. F. Sclano, P. Velardi (2008) Termextractor: A web application to learn the common terminology of interest groups and research communities, Proceeding of the 9th Conference on Terminology and Artificial Intelligence (TIA’07).

  27. Gil, R., and Martin-Bautista, M., SMOL: a systemic methodology for ontology learning from heterogeneous sources. J Intell Inf Syst, 2014. doi:10.1007/s10844-013-0296-x.

    Google Scholar 

  28. M. Suárez-Figueroa, A. Gómez-Pérez, M. Fernández-López (2012) The NeOn Methodology for Ontology Engineering, Ontology Engineering in a Networked World, pp 9–34

  29. Iqbal, R., An Analysis of Ontology Engineering Methodologies: A Literature Review. Research Journal of Applied Sciences Engineering and Technology 6(16):2993–3000, 2013.

    Google Scholar 

  30. Castillo-Barrera, F., et al., A method for building ontology-based electronic document management systems for quality standards—the case study of the ISO/TS 16949:2002 automotive standard. J. of Applied Intelligence 38:99–113, 2013. doi:10.1007/s10489-012-0360-1.

    Article  Google Scholar 

  31. Haghighi, P., et al., Development and evaluation of ontology for intelligent decision support in medical emergency management for mass gatherings. Decision Support Systems 54:1192–1204, 2013.

    Article  Google Scholar 

  32. Zhao, H., and Passi, K., Semantic Web and Ontology Engineering for the Colorectal Cancer Follow-Up Clinical Practice Guidelines. Health Information Science 7798:53–64, 2013.

    Article  Google Scholar 

  33. A. Rahimi, et al. (2012) Developing an Ontology for Data Quality in Chronic Disease Management, 24th International Conference of the European Federation for Medical Informatics, Quality of Life through Quality of Information (MIE2012).

  34. D. Forbes, P. Wongthongtham, J. Singh (2012) Development of Patient-Practitioner Assistive Communications (PPAC) Ontology for Type 2 Diabetes Management, CIHealth 2012: Proceeding of the Workshop on New Trends of Computational Intelligence in Health Applications, pp 43–54

  35. D. Sutton, A. Aldea, C. Martin (2011) An ontology of diabetes self-management. Proc. of the first int. workshop on Managing interoperability and complexity in health systems, pp 83–86.

  36. Chen, J., Su, S., and Chang, C., Diabetes Care Decision Support System. International Conference on Industrial and Information Systems (IIS) 1:323–326, 2010.

    Google Scholar 

  37. Guo, Y., Hu, J., and Peng, Y., A CBR system for injection mould design based on ontology: A case study. Computer-Aided Design 44:496–508, 2012.

    Article  Google Scholar 

  38. The Protégé Ontology Editor and Knowledge Acquisition System (2013). http://protege.stanford.edu.Accessed 10 May 2013

  39. OWL 2 Web Ontology Language (2013). http://www.w3.org/TR/owl2-overview.Accessed 30 May 2013

  40. Yang, S., Developing an energy-saving and case-based reasoning information agent with Web service and ontology techniques. Expert Systems with Applications 40(9):3351–3369, 2013.

    Article  Google Scholar 

  41. Siorpaes, K., and Simperl, E., Human Intelligence in the Process of Semantic Content Creation. Journal World Wide Web 13(1–2):33–59, 2010. doi:10.1007/s11280-009-0078-0.

    Article  Google Scholar 

  42. Canadian Journal of Diabetes, Canadian Diabetes Association 2013 Clinical Practice Guidelines (2013), http://guidelines.diabetes.ca, Accessed 20 May 2013

  43. I. Horrocks, et al. (2004) SWRL: A semantic Web Rule Language Combining OWL and RuleML, W3C Member Submission, www.w3.org/Submission/SWRL, last seen June 2013.

  44. W3C, Time Ontology in OWL (2013) http://www.w3.org/TR/owl-time.Accessed 11 May 2013.

  45. O’Connor, M., and Das, A., A Method for Representing and Querying Temporal Information in OWL, third International Joint Conference. BIOSTECValencia, Spain, pp. 97–110, 2011. doi:10.1007/978-3-642-18472-7_8.

    Google Scholar 

  46. Batsakis, S., SOWL: A Framework for Handling Spatio-Temporal Information in OWL, Ph.D. dissertation, Technical Univ. of Crete (TUC), Rule-Based Reasoning, Programming, and Applications. Lecture Notes in Computer Science 6826:242–249, 2011. doi:10.1007/978-3-642-22546-8_19.

    Article  Google Scholar 

  47. Lin, Y., and Sakamoto, N., Ontology Driven Modeling for the Knowledge of Genetic Susceptibility to Disease. Kobe J. Med. Sci. 54(6):E290–E303, 2008.

    CAS  Google Scholar 

  48. El-Sappagh, S., El-Masri, S., Riad, A., and Elmogy, M., Electronic Health Record Data Model Optimized for Knowledge Discovery. IJCSI International Journal of Computer Science Issues 9(5):329–338, 2012.

    Google Scholar 

  49. Gu, D., et al., Intelligent Technique for Knowledge Reuse of Dental Medical Records Based on Case-Based Reasoning. Journal of medical systems 34(2):213–222, 2010.

    Article  PubMed  Google Scholar 

  50. N. Kings, J. Davies (2009) Semantic Web for Knowledge Sharing, Semantic Knowledge Management, pp 103–111. doi: 10.1007/978-3-540-88845-1_8

  51. Up to Date Clinical Practice Guideline (2014) http://www.uptodate.com. Accessed 2 April 2014

  52. National Guideline Clearinghouse (2014), www.guideline.gov, Accessed 1 April 2014

  53. National Institute for Health and Care Excellence (2014) http://www.nice.org.uk.Accessed 2 April 2014

  54. Canadian Journal of Diabetes, Canadian Diabetes Association 2013 Clinical Practice Guidelines, http://guidelines.diabetes.ca. Accessed 20 May 2013

  55. Ministry of health in Malaysia, Clinical practice guideline, management of type 2 diabetes mellitus, 4th edition, http://www.diabetes.org.my, 2009. Accessed 20 May 2013

  56. American diabetes association, standards of medical care in diabetes, diabetes care, volume 36, supplement 1, January 2013, http://care.diabetesjournals.org. Accessed 20 May 2013

  57. VA/DOD Evidence-based Practice, VA/DOD clinical practice guideline management of diabetes mellitus (2010), http://www.healthquality.va.gov. Accessed May 2013

  58. AACE Diabetes care plan Guidelines, AACE Task Force for Developing a Diabetes Comprehensive Care Plan, Endocrine practice volume 17, suppl (2), 2011

  59. A. AlJarullah (2011) Decision Tree Discovery for the Diagnosis of Type II Diabetes, IEEE international conference of innovations in information technology, pp 303–307

  60. J. Juarez, et al. (2007) Case representation ontology for case retrieval systems in medical domains, ACM Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications, pp 168–173

  61. Pan, F., and Hobbs, J., Time in OWL-S, In Proceedings of the AAAI Spring Symposium on Semantic Web Services. Stanford University, CA, pp. 29–36, 2004.

    Google Scholar 

  62. Aissam, B., et al., From UML class diagrams to OWL ontologies: a Graph transformation based Approach. ICWIT, CEUR Workshop Proceedings 867:330–335, 2012.

    Google Scholar 

  63. Dragan Gašević, et al. (2004) Converting UML to OWL Ontologies, ACM Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters (WWW Alt. '04), pp 488–489. doi:10.1145/1013367.1013539

  64. Mukesh, J., et al., Diabetes Detection and Care Applying CBR Techniques. International Journal of Soft Computing and Engineering (IJSCE) 2(6):132–137, 2013.

    Google Scholar 

  65. Meersman, R., The use of lexicons and other computer-linguistic tools in semantics, design and cooperation of database systems, Proceedings of the Second International Symposium on Cooperative Database Systems for Advanced Applications (CODAS99). Springer, Verlag, pp. 1–14, 1999.

    Google Scholar 

  66. P. Spyns (2005) Object role modelling for ontology engineering in the DOGMA framework, OTM'05 Proceedings of the 2005 OTM Confederated international conference on the Move to Meaningful Internet Systems, Springer-Verlag, 3762:710–779

  67. Branden, M., et al., Integrating case-based reasoning with an electronic patient record system. J. of Artificial Intelligence in Medicine 51:117–123, 2011.

    Article  Google Scholar 

  68. Data Master (2013), http://protegewiki.stanford.edu/index.php/DataMaster. Accessed 15 June 2013

  69. Apache Jena (2014), http://jena.sourceforge.net. Accessed 4 April 2014

  70. G´omez-P´erez, A., Ontological engineering: With examples from the areas of knowledge management, e-commerce and the Semantic Web(Advanced information and knowledge processing), First Editionth edition. Springer-Verlag New York, Inc, Secaucus, 2003.

    Google Scholar 

  71. OntoClean Ontology (2013) http://protege.stanford.edu/ontologies/ontoClean/ontoCleanOntology.html. Accessed Feb 2013

  72. M. Fernández-López (2013) Deliverable 1.4: A survey on methodologies for developing, maintaining, evaluating and reengineering ontologies, IST Project IST-2000-29243 OntoWeb, http://www.kde.cs.uni-kassel.de/stumme/papers/2002/OntoWeb_Del_1-4.pdf

  73. Changrui, Y., and Yan, L., Comparative Research on Methodologies for Domain Ontology Development, Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence 6839:349–356, 2012.

    Google Scholar 

  74. S. Bechhofer, G. CA, I. Horrocks (2001) DAML + OIL is not enough, pp 151–159. DBLP:conf/semweb/2001swws, SWWS

  75. Ml, H., Yh, H., Wm, L., Rk, L., and Th, W., Usage of case-based reasoning, neural network and adaptive neuro-fuzzy inference system classification techniques in breast cancer dataset classification diagnosis. J Med Syst. 36(2):407–14, 2012.

    Article  Google Scholar 

  76. Sharaf-El-Deen, D., Moawad, I., and Khalifa, M., A New Hybrid Case-Based Reasoning Approach for Medical Diagnosis Systems. J Med Syst. 38(2):9, 2014.

    Article  PubMed  Google Scholar 

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Acknowledgment

This project was supported by King Saud University, Deanship of Scientific Research, College of Sciences Research Centre.

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Authors and Affiliations

  1. Department of Mathematics, College of Science, King Saud University, Riyadh, KSA, Saudi Arabia

    Shaker H. El-Sappagh

  2. Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

    Samir El-Masri

  3. Faculty of Computers & Information, Mansoura University, Mansoura, Egypt

    Shaker H. El-Sappagh, Mohammed Elmogy & A. M. Riad

  4. College of Public Health and Health Informatics, University of King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia

    Basema Saddik

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  1. Shaker H. El-Sappagh
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Correspondence to Samir El-Masri.

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El-Sappagh, S.H., El-Masri, S., Elmogy, M. et al. An Ontological Case Base Engineering Methodology for Diabetes Management. J Med Syst 38, 67 (2014). https://doi.org/10.1007/s10916-014-0067-4

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