Abstract
Several infectious diseases and pandemics have so far emerged. Pandemics are by nature rapidly evolving. In this context, COVID-19 cases, seen recently in a growing number of countries around the world, have been increasing exponentially. So, researchers and responsible actors should take quick decisions to mitigate the spread of such diseases. To do so, several computer science solutions, including ontologies, have been proposed to cope with these issues and save humanity. The ontology is the key formalism which allows modelling knowledge along with its semantics in a formal way. Indeed, the ontology provides unambiguous definitions of a discourse’s domain terms in a machine understandable way. Particularly, biomedical ontologies have ever been developed to capture and represent pandemics and infectious diseases. In this context, this paper aims to scrutinize and study these state-of-the-art ontologies.
Keywords: Pandemics, Infectious diseases, Biomedical, Ontology, Representation
References
- 1.Gruber T.R. « A translation approach to portable ontology specifications ». Knowledge Acquisition. 1993;5(2):199–220. doi: 10.1006/knac.1993.1008. [Google Scholar]
- 2.Borst P., Akkermans H., Top J. « Engineering ontologies ». International Journal of Human-Computer Studies. 1997;46(2‑3):365–406. févr, doi: 10.1006/IJHC.1996.0096. [Google Scholar]
- 3.Studer R., Benjamins V.R., Fensel D. « Knowledge engineering: Principles and methods ». Data & Knowledge Engineering. 1998;25(1‑2):161–197. doi: 10.1016/S0169-023X(97)00056-6. [Google Scholar]
- 4.N. Guarino, « Formal Ontology and Information Systems », in Proceedings of the first international conference (FOIS’98), 1998, vol. 46, no 15, p. 3‑15.
- 5.Abdelrahman Z., Li M., Wang X. « Comparative Review of SARS-CoV-2, SARS-CoV, MERS-CoV, and Influenza A Respiratory Viruses ». Front. Immunol. 2020;11 doi: 10.3389/fimmu.2020.552909. doi: 10.3389/fimmu.2020.552909. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.A. Gomez-Prerez, « Ontology Evaluation », in Handbook on Ontologies, 2004, p. 251‑273. [En ligne]. Disponible sur: 10.1007/978-3-540-92673-3_13 [DOI]
- 7.O. Corcho, M. Fernández-López, et A. Gómez-Pérez, « Methodologies, tools and languages for building ontologies. Where is their meeting point? », Data and Knowledge Engineering, vol. 46, no 1, p. 41‑64, 2003, doi: 10.1016/S0169-023X(02)00195-7.
- 8.M. Fernandez-Lopez, A. Gomez-Perez, et N. Juristo, « Methontology: from Ontological Art Towards Ontological Engineering », in Proceedings of the AAAI97 Spring Symposium, 1997, p. 33--40.
- 9.R. Dieng et al., Méthodes et Outils pour la Gestion des Connaissances : Une Approche Pluridisciplinaire du Knowledge Management. Dunod, 2001.
- 10.F. Gandon, « Ontology Engineering: a Survey and a Return on Experience », INRIA, Technical Report, 2002.
- 11.Musen M.A. « The protégé project ». AI Matters. 2015;1(4):4–12. doi: 10.1145/2757001.2757003. doi: 10.1145/2757001.2757003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Poveda-Villalón M., Gómez-Pérez A., Suárez-Figueroa M.C. Innovations, Developments, and Applications of Semantic Web and Information Systems. IGI Global; Hershey, PA: 2018. « OOPS!: A Pitfall-Based System for Ontology Diagnosis »; pp. 120–148. [Google Scholar]
- 13.L. Bayoudhi, N. Sassi, et W. Jaziri, « A Hybrid Storage Strategy to Manage the Evolution of an OWL 2 DL Domain Ontology », in Proceedings of the 21st International Conference KES-2017, 2017, vol. 112, p. 574‑583. doi: 10.1016/j.procs.2017.08.170.
- 14.Bayoudhi L., Sassi N., Jaziri W. « How to Repair Inconsistency in OWL 2 DL Ontology Versions? ». Data and Knowledge Engineering. 2018;116:138–158. doi: 10.1016/j.datak.2018.05.010. [Google Scholar]
- 15.Bayoudhi L., Sassi N., Jaziri W. « Efficient management and storage of a multiversion OWL 2 DL domain ontology ». Expert Systems. 2019;36(2):e12355. doi: 10.1111/exsy.12355. [Google Scholar]
- 16.Messaoudi R., et al. « Ontology-Based Approach for Liver Cancer Diagnosis and Treatment ». J Digit Imaging. 2019;32(1):116–130. doi: 10.1007/s10278-018-0115-6. doi: 10.1007/s10278-018-0115-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.S. Sbissi, M. Mahfoudh, et S. Gattoufi, « Mapping Clinical Practice Guidelines to SWRL Rules », in New Knowledge in Information Systems and Technologies, Cham, 2019, p. 283‑292. doi: 10.1007/978-3-030-16181-1_27.
- 18.Collier N., et al. « A multilingual ontology for infectious disease surveillance: rationale, design and challenges ». Lang Resources & Evaluation. 2007;40(3):405–413. doi: 10.1007/s10579-007-9019-7. doi: 10.1007/s10579-007-9019-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Topalis P., et al. « IDOMAL: an ontology for malaria ». Malaria Journal. 2010;9(1):1–11. doi: 10.1186/1475-2875-9-230. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Day-Richter J., Harris M.A., Haendel M., Gene Ontology OBO-Edit Working Group, Lewis S. « OBO-Edit--an ontology editor for biologists ». Bioinformatics. 2007;23(16):2198–2200. doi: 10.1093/bioinformatics/btm112. doi: 10.1093/bioinformatics/btm112. [DOI] [PubMed] [Google Scholar]
- 21.Smith B., et al. « The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration ». Nat Biotechnol. 2007;25(11):1251–1255. doi: 10.1038/nbt1346. doi: 10.1038/nbt1346. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Cowell et B. Smith L.G. In: Infectious Disease Informatics. Sintchenko V., editor. Springer New York; New York, NY: 2010. « Infectious Disease Ontology »; pp. 373–395. doi: 10.1007/978-1-4419-1327-2_19. [Google Scholar]
- 23.Arp R., Smith B., Spear A.D. Building ontologies with basic formal ontology. Mit Press; 2015. [Google Scholar]
- 24.Topalis P., Mitraka E., Dritsou V., Dialynas E., Louis C. « IDOMAL: the malaria ontology revisited ». Journal of Biomedical Semantics. 2013;4(1):1–6. doi: 10.1186/2041-1480-4-16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.B. Motik et al., « OWL 2 Web Ontology Language - Structural Specification and Functional-Style Syntax (Second Edition) », Online, 2012. https://www.w3.org/TR/owl2-syntax/
- 26.M. Conway, J. Dowling, et W. Chapman, « Developing a biosurveillance application ontology for influenza-like-illness », in Proceedings of the 6th Workshop on Ontologies and Lexical Resources, 2010, p. 58‑66.
- 27.Lin Y., Xiang Z., He Y. « Brucellosis Ontology (IDOBRU) as an extension of the Infectious Disease Ontology ». Journal of Biomedical Semantics. 2011;2(1):1–18. doi: 10.1186/2041-1480-2-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Mitraka E., Topalis P., Dritsou V., Dialynas E., Louis C. « Describing the Breakbone Fever: IDODEN, an Ontology for Dengue Fever ». PLoS neglected tropical diseases. 2015;9(2):e0003479. doi: 10.1371/journal.pntd.0003479. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.G. Camara, S. Despres, R. Djedidi, et M. Lo, « Towards a schistosomiasis ontology (IDOSCHISTO) extending the Infectious Disease Ontology », 2016. [PubMed]
- 30.Grenon P., Smith B., Goldberg L. « Biodynamic ontology: applying BFO in the biomedical domain ». Stud Health Technol Inform. 2004;102:20–38. [PubMed] [Google Scholar]
- 31.Jayawardhana et P. V. Gorsevski M.U.K. « An ontology-based framework for extracting spatio-temporal influenza data using Twitter ». International Journal of Digital Earth. 2019;12(1):2–24. [Google Scholar]
- 32.W. R. C. Béré, G. Camara, S. Malo, et M. Lo, « IDOMEN: An Extension of Infectious Disease Ontology for MENingitis », in Proceedings of the 17th World Congress on Medical and Health Informatics (MEDINFO2019), Lyon, France, 2019, vol. 264, p. 313‑317. [DOI] [PubMed]
- 33.Radhika P., Varm P.S., Kalyani N.L., Krishna P.R., Kumar M.S. « Representation of Knowledge through Ontology for Swine Flu Disease in Semiarid Tropical Regions ». IJPHRD. 2019;10(3) Art. no 3, mars, doi: 10.37506/ijphrd.v10i3.6950. [Google Scholar]
- 34.de Lusignan S., et al. « COVID-19 Surveillance in a Primary Care Sentinel Network: In-Pandemic Development of an Application Ontology ». JMIR Public Health Surveill. 2020;6(4):e21434. doi: 10.2196/21434. doi: 10.2196/21434. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Sargsyan A., et al. « The COVID-19 Ontology ». Bioinformatics. 2020;36(24):5703–5705. doi: 10.1093/bioinformatics/btaa1057. doi: 10.1093/bioinformatics/btaa1057. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.B. Dutta et M. DeBellis, « CODO: an ontology for collection and analysis of COVID-19 data », in Proceedings of 12th Int. Conf. on Knowledge Engineering and Ontology Development (KEOD), 2020, p. 76‑85.
- 37.M. J. O’Connor, C. Halaschek-Wiener, et M. A. Musen, « Mapping Master: A Flexible Approach for Mapping Spreadsheets to OWL », in The Semantic Web – ISWC 2010, Berlin, Heidelberg, 2010, p. 194‑208. doi: 10.1007/978-3-642-17749-1_13.
- 38.Sirin E., Parsia B., Grau B.C., Kalyanpur A., Katz Y. « Pellet: A Practical OWL-DL Reasoner ». Web Semantics: Science, Services and Agents on the World Wide Web. 2007;5(2):51–53. [Google Scholar]
- 39.M. Horridge et M. Musen, « Snap-SPARQL: A Java Framework for working with SPARQL and OWL », in Revised Selected Papers of the 12th International Experiences and Directions Workshop on Ontology Engineering, 2015, vol. 9557, p. 154‑165. doi: 10.1007/978-3-319-33245-1_16. [DOI]
- 40.I. Franz, « AllegroGraph », 2021. https://allegrograph.com/ (consulté le avr. 18, 2021).
- 41.He Y., et al. « CIDO, a community-based ontology for coronavirus disease knowledge and data integration, sharing, and analysis ». Scientific Data. 2020;7(1) doi: 10.1038/s41597-020-0523-6. Art. no 1, doi: 10.1038/s41597-020-0523-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Hastings J., et al. « ChEBI in 2016: Improved services and an expanding collection of metabolites ». Nucleic Acids Res. 2016;44(D1):D1214–1219. doi: 10.1093/nar/gkv1031. doi: 10.1093/nar/gkv1031. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Köhler S., et al. « The Human Phenotype Ontology in 2017 ». Nucleic Acids Research. 2017;45(D1):D865–D876. doi: 10.1093/nar/gkw1039. doi: 10.1093/nar/gkw1039. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Kibbe W.A., et al. « Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data ». Nucleic Acids Research. 2015;43(D1):D1071–D1078. doi: 10.1093/nar/gku1011. doi: 10.1093/nar/gku1011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Federhen S. « The NCBI Taxonomy database ». Nucleic Acids Research. 2012;40(D1):D136–D143. doi: 10.1093/nar/gkr1178. doi: 10.1093/nar/gkr1178. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Liu Y., et al. « Ontological modeling and analysis of experimentally or clinically verified drugs against coronavirus infection ». Scientific Data. 2021;8(1) doi: 10.1038/s41597-021-00799-w. Art. no 1, doi: 10.1038/s41597-021-00799-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Scheuermann R.H., Ceusters W., Smith B. « Toward an Ontological Treatment of Disease and Diagnosis ». Summit on Translat Bioinforma. 2009;2009:116–120. [PMC free article] [PubMed] [Google Scholar]
- 48.Bandrowski A., et al. « The Ontology for Biomedical Investigations ». PLoS One. 2016;11(4):e0154556. doi: 10.1371/journal.pone.0154556. doi: 10.1371/journal.pone.0154556. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.He Y., et al. « VO: Vaccine Ontology ». Nature Precedings. 2009:1. août, doi: 10.1038/npre.2009.3552.1. [Google Scholar]
- 50.E. Kalemi et E. Martiri, « FOAF-academic ontology: a vocabulary for the academic community », in 2011 Third International Conference on Intelligent Networking and Collaborative Systems, 2011, p. 440‑445.
- 51.Horridge et S. Bechhofer M. « The OWLAPI: A Java API for OWL ontologies ». Semantic Web. 2011;2(1):11–21. doi: 10.3233/SW-2011-0025. [Google Scholar]
- 52.World Wide Web Consortium, « SPARQL 1.1 Overview », 2013. https://www.w3.org/TR/sparql11-overview/ (consulté le mai 14, 2019).
- 53.B. Glimm et C. Ogbuji, « SPARQL 1.1 Entailment Regimes », W3C Recommendation, Technical Report, 2013. Consulté le: juin 18, 2019. [En ligne]. Disponible sur: https://www.w3.org/TR/sparql11-entailment/
- 54.R. Shearer, B. Motik, et I. Horrocks, « Hermit: a Highly-efficient OWL REasoner », in Proceedings of the 5th International Workshop on OWL: Experiences and Directions (OWLED 2008), 2008, vol. 432, p. 91‑100.
- 55.D. Tsarkov et I. Horrocks, « FaCT++ Description Logic Reasoner: System Description », in Proceedings of the International Joint Conference on Automated Reasoning(IJCAR 2006), 2006, 2006, p. 292‑297. doi: 10.1007/11814771_26. [DOI]
