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Clinical Research Informatics pp 313–339Cite as

Knowledge Representation and Ontologies

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Abstract

The representation of medical data and knowledge is fundamental in the field of medical informatics. Ontologies and related artifacts are important tools in knowledge representation, yet they are often given little attention and taken for granted. In this chapter, we give an overview of the development of medical ontologies, including available ontology repositories and tools. We highlight some ontologies that are particularly relevant to clinical research and describe with examples the benefits of using ontologies to facilitate research workflow management, data integration, and electronic phenotyping.

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References

  1. Bodenreider O. Biomedical ontologies in action: role in knowledge management, data integration and decision support. Yearb Med Inform 2008;17(01):67–79.

    Article  Google Scholar 

  2. Smith B. Ontology (Science). Nature Precedings, 2008. Available from Nature Precedings. http://hdl.handle.net/10101/npre.2008.2027.2.

  3. Bodenreider O, Stevens R. Bio-ontologies: current trends and future directions. Brief Bioinform. 2006;7(3):256–74.

    Article  CAS  PubMed  Google Scholar 

  4. Cimino JJ, Zhu X. The practical impact of ontologies on biomedical informatics. Yearb Med Inform 2006;15(01):124–135.

    Article  Google Scholar 

  5. Smith B, et al. Relations in biomedical ontologies. Genome Biol. 2005;6(5):R46.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Simmons P, Melia J. Continuants and occurrents. Proc Aristot Soc Suppl Vol. 2000;74:59–75. +77–92.

    Article  Google Scholar 

  7. IFOMIS. BFO. Available from: http://www.ifomis.org/bfo/.

  8. Laboratory for Applied Ontology. DOLCE. Available from: http://www.loa-cnr.it/DOLCE.html.

  9. McCray AT. An upper-level ontology for the biomedical domain. Comp Funct Genomics. 2003;4(1):80–4.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Baader F, et al. The description logic handbook: theory, implementation, and applications. 2nd ed. xix, 601 p ed. 2007, Cambridge University Press: Cambridge, New York. ill. 26 cm.

    Google Scholar 

  11. Berners-Lee T, Hendler J, Lassila O. The semantic web: a new form of web content that is meaningful to computers will unleash a revolution of new possibilities. Sci Am. 2001;284(5):34–43.

    Article  Google Scholar 

  12. World Wide Web Consortium. OWL 2 web ontology language document overview. 2009a. Available from: http://www.w3.org/TR/owl2-overview/.

  13. World Wide Web Consortium. RDF vocabulary description language 1.0: RDF schema. 2004. Available from: http://www.w3.org/TR/rdf-schema/.

  14. World Wide Web Consortium. SKOS simple knowledge organization system reference. 2009b. Available from: http://www.w3.org/TR/2009/REC-skos-reference-20090818/.

  15. Day-Richter J. The OBO flat file format specification. 2006. Available from: http://www.geneontology.org/GO.format.obo-1_2.shtml.

  16. Mungall C, et al.. OBO flat file format 1.4 syntax and semantics. Available from: http://owlcollab.github.io/oboformat/doc/obo-syntax.html.

  17. Golbreich C, et al. OBO and OWL: leveraging semantic web technologies for the life sciences, in Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference. Busan: Springer-Verlag; 2007. p. 169–82.

    Google Scholar 

  18. Noy N, et al. The ontology life cycle: integrated tools for editing, publishing, peer review, and evolution of ontologies. AMIA Ann Symp Proc. 2010;2010:552–6.

    Google Scholar 

  19. Stanford Center for Biomedical Informatics Research. Protégé. Available from: http://protege.stanford.edu/.

  20. Day-Richter J, et al. OBO-edit-an ontology editor for biologists. Bioinformatics. 2007;23(16):2198–200.

    Article  CAS  PubMed  Google Scholar 

  21. Lawrence Berkeley National Lab. OBO-edit. Available from: http://oboedit.org/.

  22. Smith B, et al. The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat Biotechnol. 2007;25(11):1251–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. International S. Partnerships – working with other standards organizations. Available from: https://www.snomed.org/about/partnerships.

  24. Richesson RL, Krischer J. Data standards in clinical research: gaps, overlaps, challenges and future directions. J Am Med Inform Assoc. 2007;14(6):687–96.

    Article  PubMed  PubMed Central  Google Scholar 

  25. FAIRsharing website. https://www.FAIRsharing.org.

  26. McQuilton P, Gonzalez-Beltran A, Rocca-Serra P, Thurston M, Lister A, Maguire E, Sansone SA. BioSharing: curated and crowd-sourced metadata standards, databases and data policies in the life sciences. Database (Oxford). 2016.

    Google Scholar 

  27. Sim I, et al. Ontology-based federated data access to human studies information. AMIA Ann Symp Proc. 2012;2012:856–65.

    Google Scholar 

  28. Tu SW, et al. OCRe: ontology of clinical research. In 11th International Protege Conference. 2009.

    Google Scholar 

  29. Bandrowski A, et al. The ontology for biomedical investigations. PLoS One. 2016;11(4):e0154556.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Ontology for Biomedical Investigations: Community Standard for Scientific Data Integration. Available from: http://obi-ontology.org/.

  31. Whetzel PL, et al. Development of FuGO: an ontology for functional genomics investigations. OMICS. 2006;10(2):199–204.

    Article  CAS  PubMed  Google Scholar 

  32. Brinkman RR, et al. Modeling biomedical experimental processes with OBI. J Biomed Semant. 2010;1(Suppl 1):S7.

    Article  Google Scholar 

  33. Becnel LB, et al. BRIDG: a domain information model for translational and clinical protocol-driven research. J Am Med Inform Assoc. 2017;24(5):882–90.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Biomedical Research Integrated Domain Group Website. Available from: https://bridgmodel.nci.nih.gov/faq/components-of-bridg-model.

  35. Fridsma DB, et al. The BRIDG project: a technical report. J Am Med Inform Assoc. 2008;15(2):130–7.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Tu SW, et al. Bridging epoch: mapping two clinical trial ontologies. In 10th International Protege Conference. 2007.

    Google Scholar 

  37. de Coronado S, et al. NCI thesaurus: using science-based terminology to integrate cancer research results. Med Info. 2004;11(Pt 1):33–7.

    Google Scholar 

  38. Fragoso G, et al. Overview and utilization of the NCI thesaurus. Comp Funct Genomics. 2004;5(8):648–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Sioutos N, et al. NCI Thesaurus: a semantic model integrating cancer-related clinical and molecular information. J Biomed Inform. 2007;40(1):30–43.

    Article  CAS  PubMed  Google Scholar 

  40. International S. SNOMED CT (Systematized Nomenclature of Medicine-Clinical Terms), SNOMED International. Available from: https://www.snomed.org/.

  41. Lee D, et al. A survey of SNOMED CT implementations. J Biomed Inform. 2013;46(1):87–96.

    Article  PubMed  Google Scholar 

  42. Blumenthal D, Tavenner M. The “meaningful use” regulation for electronic health records. N Engl J Med. 2010;363(6):501–4.

    Article  CAS  PubMed  Google Scholar 

  43. Office of the National Coordinator for Health Information Technology (ONC) – Department of Health and Human Services. Standards & certification criteria Interim final rule: revisions to initial set of standards, implementation specifications, and certification criteria for electronic health record technology. Fed Regist. 2010;75(197):62686–90.

    Google Scholar 

  44. Huff SM, et al. Development of the Logical Observation Identifiers Names and Codes (LOINC) vocabulary. J Am Med Inform Assoc. 1998;5(3):276–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Logical Observation Identifier Names and Codes (LOINC). Available from: https://loinc.org/.

  46. Nelson SJ, et al. Normalized names for clinical drugs: RxNorm at 6 years. J Am Med Inform Assoc. 2011;18(4):441–8.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Bouhaddou O, et al. Exchange of computable patient data between the Department of Veterans Affairs (VA) and the Department of Defense (DoD): terminology standards strategy. J Am Med Inform Assoc. 2008;15:174–183.

    Google Scholar 

  48. History of the development of the ICD, World Health Organization. Available from: http://www.who.int/classifications/icd/en/HistoryOfICD.pdf.

  49. Steindel SJ. International classification of diseases, 10th edition, clinical modification and procedure coding system: descriptive overview of the next generation HIPAA code sets. J Am Med Inform Assoc. 2010;17(3):274–82.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Fung KW, et al. Preparing for the ICD-10-CM transition: automated methods for translating ICD codes in clinical phenotype definitions. EGEMS (Wash DC). 2016;4(1):1211.

    Google Scholar 

  51. Averill RF, et al. Development of the ICD-10 procedure coding system (ICD-10-PCS). Top Health Inf Manag. 2001;21(3):54–88.

    CAS  Google Scholar 

  52. Cimino JJ, Ayres EJ. The clinical research data repository of the US National Institutes of Health. Stud Health Technol Inform. 2010;160(Pt 2):1299–303.

    PubMed  PubMed Central  Google Scholar 

  53. Lowe HJ, et al. STRIDE – an integrated standards-based translational research informatics platform. AMIA Ann Symp Proc. 2009;2009:391–5.

    Google Scholar 

  54. Ruttenberg A, et al. Methodology – advancing translational research with the Semantic Web. BMC Bioinforma. 2007;8:S2.

    Article  CAS  Google Scholar 

  55. McCusker JP, et al. Semantic web data warehousing for caGrid. BMC Bioinforma. 2009;10(Suppl 10):S2.

    Article  CAS  Google Scholar 

  56. Sahoo SS, et al. An ontology-driven semantic mashup of gene and biological pathway information: application to the domain of nicotine dependence. J Biomed Inform. 2008;41(5):752–65.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Semantic Web for Health Care and Life Sciences Interest Group. Translational medicine ontology and knowledge base. Available from: http://www.w3.org/wiki/HCLSIG/PharmaOntology.

  58. Bodenreider O. The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res. 2004;32(Database issue):D267–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Humphreys BL, Lindberg DA, Hole WT. Assessing and enhancing the value of the UMLS Knowledge Sources. Proc Annu Symp Comput Appl Med Care. 1991:78–82.

    Google Scholar 

  60. Humphreys BL, et al. The unified medical language system: an informatics research collaboration. J Am Med Inform Assoc. 1998;5(1):1–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Lindberg DA, Humphreys BL, McCray AT. The unified medical language system. Methods Inf Med. 1993;32(4):281–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. UMLS. Unified Medical Language System (UMLS). Available from: http://www.nlm.nih.gov/research/umls/.

  63. McCray AT, Srinivasan S, Browne AC. Lexical methods for managing variation in biomedical terminologies. Proc Ann Symp Comput Appl Med Care. 1994:235–9.

    Google Scholar 

  64. Fung KW, Bodenreider O. Utilizing the UMLS for semantic mapping between terminologies. AMIA Annu Symp Proc. 2005:266–70.

    Google Scholar 

  65. Aronson AR. Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. Proc AMIA Symp. 2001:17–21.

    Google Scholar 

  66. Aronson AR, Lang FM. An overview of MetaMap: historical perspective and recent advances. J Am Med Inform Assoc. 2010;17(3):229–36.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Fung KW, Hole WT, Srinivasan S. Who is using the UMLS and how – insights from the UMLS user annual reports. AMIA Annu Symp Proc. 2006:274–8.

    Google Scholar 

  68. Noy NF, et al. BioPortal: ontologies and integrated data resources at the click of a mouse. Nucleic Acids Res. 2009;37(Web Server issue):W170–3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Ghazvinian A, Noy NF, Musen MA. Creating mappings for ontologies in biomedicine: simple methods work. AMIA Ann Symp Proc. 2009;2009:198–202.

    Google Scholar 

  70. Shankar RD, et al. An ontology-based architecture for integration of clinical trials management applications. AMIA Ann Symp Proc. 2007:661–5.

    Google Scholar 

  71. Shankar R, et al. TrialWiz: an ontology-driven tool for authoring clinical trial protocols. AMIA Ann Symp Proc. 2008:1226.

    Google Scholar 

  72. Brochhausen M, et al. The ACGT master ontology and its applications – towards an ontology-driven cancer research and management system. J Biomed Inform. 2011;44(1):8–25.

    Article  PubMed  Google Scholar 

  73. Martin L, Anguita A, Graf N, Tsiknakis M, Brochhausen M, Rüping S, Bucur A, Sfakianakis S, Sengstag T, Buffa F, Stenzhorn H. ACGT: advancing clinico-genomic trials on cancer - four years of experience. Stud Health Technol Inform. 2011;169:734–8.

    Google Scholar 

  74. Stenzhorn H, et al. The ObTiMA system – ontology-based managing of clinical trials. Stud Health Technol Inform. 2010;160(Pt 2):1090–4.

    PubMed  Google Scholar 

  75. Weiler G, et al. Ontology based data management systems for post-genomic clinical trials within a European Grid Infrastructure for Cancer Research. Conf Proc IEEE Eng Med Biol Soc. 2007;2007:6435–8.

    Google Scholar 

  76. Eukaryotic Pathogen Database. Available from: https://eupathdb.org/eupathdb/.

  77. FDA Janus Data Repository. Available from: https://www.fda.gov/ForIndustry/DataStandards/StudyDataStandards/ucm155327.htm.

  78. Genome-Wide Association Studies. Available from: http://grants.nih.gov/grants/gwas/.

  79. Bodenreider O. Ontologies and data integration in biomedicine: success stories and challenging issues. In: Bairoch A, Cohen-Boulakia S, Froidevaux C, editors. Proceedings of the Fifth International Workshop on Data Integration in the Life Sciences (DILS 2008). Berlin: Springer; 2008b. p. 1–4.

    Google Scholar 

  80. Vivli: Center for Global Clinical Research Data. Available from: http://vivli.org/.

  81. Rubin DL, Shah NH, Noy NF. Biomedical ontologies: a functional perspective. Brief Bioinform. 2008;9(1):75–90.

    Article  PubMed  Google Scholar 

  82. Sansone SA, et al. Toward interoperable bioscience data. Nat Genet. 2012;44(2):121–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. SALUS Project: Security and interoperability in next generation PPDR communication infrastructures. Available from: https://www.sec-salus.eu/.

  84. Cook C, et al. Real-time updates of meta-analyses of HIV treatments supported by a biomedical ontology. Account Res. 2007;14(1):1–18.

    Article  PubMed  Google Scholar 

  85. Shah NH, et al. Ontology-driven indexing of public datasets for translational bioinformatics. BMC Bioinforma. 2009;10(Suppl 2):S1.

    Article  Google Scholar 

  86. Bizer C, Heath T, Berners-Lee T. Linked data – the story so far. Int J Semant Web Inf Syst. 2009;5(3):1–22.

    Article  Google Scholar 

  87. HCLS. Semantic Web Health Care and Life Sciences (HCLS) Interest Group.

    Google Scholar 

  88. Semantic Web for Health Care and Life Sciences Interest Group. Linking open drug data. Available from: http://www.w3.org/wiki/HCLSIG/LODD.

  89. Gottesman O, et al. The electronic medical records and genomics (eMERGE) network: past, present, and future. Genet Med. 2013;15(10):761–71.

    Article  PubMed  PubMed Central  Google Scholar 

  90. Fleurence RL, et al. Launching PCORnet, a national patient-centered clinical research network. J Am Med Inform Assoc. 2014;21(4):578–82.

    Article  PubMed  PubMed Central  Google Scholar 

  91. Chute CG, et al. The SHARPn project on secondary use of electronic medical record data: progress, plans, and possibilities. AMIA Ann Symp Proc. 2011;2011:248–56.

    Google Scholar 

  92. Hripcsak G, et al. Observational health data sciences and informatics (OHDSI): opportunities for observational researchers. Stud Health Technol Inform. 2015;216:574–8.

    PubMed  PubMed Central  Google Scholar 

  93. Richesson RL, et al. Electronic health records based phenotyping in next-generation clinical trials: a perspective from the NIH Health Care Systems Collaboratory. J Am Med Inform Assoc. 2013;20(e2):e226–31.

    Article  PubMed  PubMed Central  Google Scholar 

  94. Carroll RJ, et al. Portability of an algorithm to identify rheumatoid arthritis in electronic health records. J Am Med Inform Assoc. 2012;19(e1):e162–9.

    Article  PubMed  PubMed Central  Google Scholar 

  95. Cutrona SL, et al. Validation of acute myocardial infarction in the Food and Drug Administration’s mini-sentinel program. Pharmacoepidemiol Drug Saf. 2013;22(1):40–54.

    Article  PubMed  Google Scholar 

  96. Kho AN, et al. Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study. J Am Med Inform Assoc. 2012;19(2):212–8.

    Article  PubMed  Google Scholar 

  97. Newton KM, et al. Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network. J Am Med Inform Assoc. 2013;20(e1):e147–54.

    Article  PubMed  PubMed Central  Google Scholar 

  98. Ritchie MD, et al. Robust replication of genotype-phenotype associations across multiple diseases in an electronic medical record. Am J Hum Genet. 2010;86(4):560–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Banda JM, et al. Electronic phenotyping with APHRODITE and the observational health sciences and informatics (OHDSI) data network. AMIA Jt Summits Transl Sci Proc. 2017;2017:48–57.

    PubMed  PubMed Central  Google Scholar 

  100. Hripcsak G, Albers DJ. Next-generation phenotyping of electronic health records. J Am Med Inform Assoc. 2013;20(1):117–21.

    Article  PubMed  Google Scholar 

  101. Martin-Sanchez FJ, et al. Secondary use and analysis of big data collected for patient care. Yearb Med Inform. 2017;26(1):28–37.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Yu S, et al. Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources. J Am Med Inform Assoc. 2015;22(5):993–1000.

    Article  PubMed  PubMed Central  Google Scholar 

  103. Kirby JC, et al. PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability. J Am Med Inform Assoc. 2016;23(6):1046–52.

    Article  PubMed  PubMed Central  Google Scholar 

  104. Campbell JR, Payne TH. A comparison of four schemes for codification of problem lists. Proc Ann Symp Comput Appl Med Care. 1994:201–5.

    Google Scholar 

  105. Campbell JR, et al. Phase II evaluation of clinical coding schemes: completeness, taxonomy, mapping, definitions, and clarity. CPRI work group on codes and structures. J Am Med Inform Assoc. 1997;4(3):238–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Chute CG, et al. The content coverage of clinical classifications. For the computer-based patient record institute’s work group on codes & structures. J Am Med Inform Assoc. 1996;3(3):224–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Mo H, et al. Desiderata for computable representations of electronic health records-driven phenotype algorithms. J Am Med Inform Assoc. 2015;22(6):1220–30.

    PubMed  PubMed Central  Google Scholar 

  108. Murphy SN, et al. Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). J Am Med Inform Assoc. 2010;17(2):124–30.

    Article  PubMed  PubMed Central  Google Scholar 

  109. Electronic Clinical Quality Improvement Resource Center, The Office of the National Coordinator for Health Information Technology. Available from: https://ecqi.healthit.gov/content/about-ecqi.

  110. Value Set Authority Center, National Library of Medicine Available from: https://vsac.nlm.nih.gov/.

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Acknowledgments

This research was supported in part by the Intramural Research Program of the National Institutes of Health (NIH), National Library of Medicine (NLM).

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Authors and Affiliations

  1. Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA

    Kin Wah Fung MD, MS, MA & Olivier Bodenreider MD, PhD

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  1. Kin Wah Fung MD, MS, MA
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  2. Olivier Bodenreider MD, PhD
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Correspondence to Kin Wah Fung MD, MS, MA .

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Editors and Affiliations

  1. Duke University School of Nursing, Durham, NC, USA

    Rachel L. Richesson

  2. School of Information, University of South Florida, Tampa, FL, USA

    James E. Andrews

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Fung, K.W., Bodenreider, O. (2019). Knowledge Representation and Ontologies. In: Richesson, R., Andrews, J. (eds) Clinical Research Informatics. Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-319-98779-8_15

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