1. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ontological Realism and the Open Biomedical Ontologies Foundry Februari 25, 2011 – San Francisco, CA Werner CEUSTERS, MD Professor, Department of Psychiatry Director, Ontology Research Group Center of Excellence in Bioinformatics and Life Sciences University at Buffalo, NY, USA

2. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Short personal history 1959 - 2011 1977 1989 1992 1998 2002 2004 2006 1993 1995 2

3. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Outline Introduction: –Health IT and the Semantic Web Ontology and Ontologies OBO and the OBO Foundry Ontological Realism Some examples 3

4. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The ultimate goal of Healthcare IT Everything collected wherever, whenever and about whomever which is relevant to a medical problem in whomever, whenever and wherever, should be accessible without loss of relevant detail.

5. New York State Center of Excellence in Bioinformatics & Life Sciences R T U If it is possible outside healthcare … received confirmation call Note in ‘EHR’ about calories purchased (or card blocked?)

6. New York State Center of Excellence in Bioinformatics & Life Sciences R T U This raises many questions Is this … - possible ? - desirable ? - scary ?

7. New York State Center of Excellence in Bioinformatics & Life Sciences R T U This raises many questions Is this … - possible ? I don’t care too much about these

8. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Is this possible? The answer of HIT industry. http://www.interoperabilityshowcase.com/docs/webinarArchives/2010_Webinar_Series_Review_PCD_Domain_2010-8-3f.pdf

9. New York State Center of Excellence in Bioinformatics & Life Sciences R T U I respectfully disagree … Standards? –No shortage indeed, but: too many, too low quality, because, too much ad hoc. Availability of ‘the’ technology? –Focus on providing patches for old technology rather than developing better systems from solid foundations. This holds for both Healthcare IT and Semantic Web Technology.

10. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Semantic Applications use Ontologies and Semantic Web Technology 10 Domain ‘Philosophical’ approach to ontology Ontologies Ontology Authoring Tools Reasoners create (OWL) Computer Science approach to ‘ontology’

11. New York State Center of Excellence in Bioinformatics & Life Sciences R T U ‘Ontology’ In philosophy: –Ontology (no plural) is the study of what entities exist and how they relate to each other; In computer science and many biomedical informatics applications: –An ontology (plural: ontologies) is a shared and agreed upon conceptualization of a domain.

12. New York State Center of Excellence in Bioinformatics & Life Sciences R T U 12 Three major views on reality Basic questions: –What does a general term such as ‘diabetes’ refer to? –Do generic things exist? yes: in particulars perhaps: in minds no UniversalConceptCollection of particulars RealismConceptualismNominalism

13. New York State Center of Excellence in Bioinformatics & Life Sciences R T U 13 No serious scholar should work with ‘concepts’

14. New York State Center of Excellence in Bioinformatics & Life Sciences R T U 14 Slow penetration of the idea …

15. New York State Center of Excellence in Bioinformatics & Life Sciences R T U 15 More serious scholars become convinced … what is a concept description a description of?

16. New York State Center of Excellence in Bioinformatics & Life Sciences R T U but Kantians will never …

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19. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The visible results of Kantianism and OWL-ism 19

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21. New York State Center of Excellence in Bioinformatics & Life Sciences R T U MedDRA: violations of all terminological rules 21

22. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Mistakes in the NCI Thesaurus OWL version 22 Schulz S, Schober S, Tudose I, Stenzhorn H: The Pitfalls of Thesaurus Ontologization – the Case of the NCI Thesaurus. AMIA Annu Symp Proc, 2010: 727- 731 (AMIA 2010 Annual Symposium, Washington D.C. USA, November 2010): http://proceedings.amia.org/127gtf/1http://proceedings.amia.org/127gtf/1

23. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Mistakes in the NCI Thesaurus OWL version The NCIT ignores the relationships between representation and reality: –Functions as subclasses of processes: the bearer of a function is not necessarily participant of a process. –Domain incompatibilities: interpreting relation names as containing domain constraints (without being backed-up by any logical definition). –Individuals expressed as classes: like in Nicaragua subClassOf Conceptual_Part_Of some North_America. 23 Schulz S, Schober S, Tudose I, Stenzhorn H: The Pitfalls of Thesaurus Ontologization – the Case of the NCI Thesaurus. AMIA Annu Symp Proc, 2010: 727- 731 (AMIA 2010 Annual Symposium, Washington D.C. USA, November 2010): http://proceedings.amia.org/127gtf/1http://proceedings.amia.org/127gtf/1

24. New York State Center of Excellence in Bioinformatics & Life Sciences R T U A problem of education Consider the wine regions. Initially, we may define main wine regions, such as France, United States, Germany, and so on, as classes and specific wine regions within these large regions as instances. For example, Bourgogne region is an instance of the French region class. However, we would also like to say that the Cotes d’Or region is a Bourgogne region. Therefore, Bourgogne region must be a class (in order to have subclasses or instances). However, making Bourgogne region a class and Cotes d’Or region an instance of Bourgogne region seems arbitrary: it is very hard to clearly distinguish which regions are classes and which are instances. Therefore, we define all wine regions as classes. 24 Ontology Development 101: A Guide to Creating Your First Ontology Natalya F. Noy and Deborah L. McGuinness

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26. New York State Center of Excellence in Bioinformatics & Life Sciences R T U OBO Foundry approach to countering silo formation –a single, expanding family of ontologies designed to be interoperable and logically well-formed and to incorporate accurate representations of biological reality.

27. New York State Center of Excellence in Bioinformatics & Life Sciences R T U OBO Foundry principles Ontologies are admitted into the Foundry only if their developers commit to an evolving set of common principles, including: –terms and s should be built up compositionally out of more basic terms from a small set of robust feeder ontologies; –for each domain there should be convergence upon exactly one Foundry ontology; –all working with same upper-level categories and relations drawn from Basic Formal Ontology (BFO) and OBO Relation Ontology (RO).

28. New York State Center of Excellence in Bioinformatics & Life Sciences R T U ‘Ontology’ In philosophy: –Ontology (no plural) is the study of what entities exist and how they relate to each other; In computer science and many biomedical informatics applications: –An ontology (plural: ontologies) is a shared and agreed upon conceptualization of a domain. Ontological Realism: a specific methodology that uses ontology as the basis for building high quality ontologies, using reality as benchmark.

29. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Realism-based Ontology There is an external reality which is ‘objectively’ the way it is; That reality is accessible to us; We build in our brains cognitive representations of reality; We communicate with others about what is there, and what we believe there is there. Ontological Realism Smith B, Ceusters W. Ontological Realism as a Methodology for Coordinated Evolution of Scientific Ontologies. Applied Ontology, 2010. 29

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31. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Representations First Order Reality L1. Entities (particular or generic) with objective existence which are not about anything L2. Clinicians’ beliefs about (1) L3. Linguistic representations about (1), (2) or (3) Three levels of reality in Ontological Realism

32. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Data generation and use observation & measurement data organization model development use add Generic beliefs verify further R&D (instrument and study optimization) application Δ = outcome 32

33. New York State Center of Excellence in Bioinformatics & Life Sciences R T U observation & measurement A crucial distinction: data and what they are about data organization model development use add Generic beliefs verify further R&D (instrument and study optimization) application Δ = outcome First- Order Reality Representation is about 33

34. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ontological Realism makes crucial distinctions Between data and what data are about: –Level 1 entities (L1): everything what exists or existed some are referents (‘are’ used informally) some are L2, some are L3, none are L2 and L3 –Level 2 entities (L2): beliefs all are L1 some are about other L1-entities but none about themselves –Level 3 entities (L3): expressions all are L1, none are L2 some are about other L1-entities and some about themselves 34

35. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ontological Realism makes crucial distinctions Between data and what data are about; Between continuants and occurrents: –obvious differences: a person versus his life an elevator versus his going up and down space versus time –more subtle differences (inexistent for flawed models e.g. HL7-RIM) : observation (data-element) versus observing diagnosis versus making a diagnosis message versus transmitting a message 35

36. New York State Center of Excellence in Bioinformatics & Life Sciences R T U 36 RELATION TO TIME GRANULARITY CONTINUANTOCCURRENT INDEPENDENTDEPENDENT ORGAN AND ORGANISM Organism (NCBI Taxonomy) Anatomical Entity (FMA, CARO) Organ Function (FMP, CPRO) Phenotypic Quality (PaTO) Biological Process (GO) CELL AND CELLULAR COMPONENT Cell (CL) Cellular Component (FMA, GO) Cellular Function (GO) MOLECULE Molecule (ChEBI, SO, RnaO, PrO) Molecular Function (GO) Molecular Process (GO) OBO Foundry ontologies in BFO-dress

37. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ontology of General Medical Science First ontology in which the L1/L2/L3 distinction is used Scheuermann R, Ceusters W, Smith B. Toward an Ontological Treatment of Disease and Diagnosis. 2009 AMIA Summit on Translational Bioinformatics, San Francisco, California, March 15-17, 2009;: 116-120. Omnipress ISBN:0-9647743-7-22009 AMIA Summit on Translational Bioinformatics

38. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Motivation Clarity about: –disease etiology and progression –disease and the diagnostic process –phenotype and signs/symptoms

39. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Big Picture

40. New York State Center of Excellence in Bioinformatics & Life Sciences R T U a disease is a disposition rooted in a physical disorder in the organism and realized in pathological processes. etiological processdisorderdispositionpathological process abnormal bodily featuressigns & symptomsinterpretive processdiagnosis producesbearsrealized_in producesparticipates_inrecognized_as produces Approach

41. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Clinical Picture =def. – A representation of a clinical phenotype that is inferred from the combination of laboratory, image and clinical findings about a given patient. Diagnosis =def. – –A conclusion of an interpretive process that has as input a clinical picture of a given patient and as output an assertion to the effect that the patient has a disease of such and such a type. Example: Diagnosis

42. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Obvious? ‘Diseases and diagnoses are the principal ways in which illnesses are classified and quantified, and are vital in determining how clinicians organize health care.’ Ann Fam Med 1(1):44-51, 2003. ‘MedDRA […] is a standardized dictionary of medical terminology [ … which …] includes terminology for symptoms, signs, diseases and diagnoses.’ Medical Dictionary for Regulatory Activities

43. New York State Center of Excellence in Bioinformatics & Life Sciences R T U A well-formed diagnosis of ‘pneumococal pneumonia’ A configuration of representational units; Believed to mirror the person’s disease; Believed to mirror the disease’s cause; Refers to the universal of which the disease is believed to be an instance. #56 John’s Pneumonia #78 John’s relevant portion of pneumococs Pneumococcal pneumonia caused by Instance-of at t1 Disease isa

44. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Some motivations and consequences (1) No use of debatable or ambiguous notions such as proposition, statement, assertion, fact,... The same diagnosis can be expressed in various forms. #56#78 Pneumococcal pneumonia caused by Instance-of at t1 #56#78 Pneumonia caused by Portion of pneumococs Instance-of at t1 Disease isa caused by

45. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Some motivations and consequences (2) A diagnosis can be of level 2 or level 3, i.e. either in the mind of a cognitive agent, or in some physical form. Allows for a clean interpretation of assertions of the sort ‘these patients have the same diagnosis’:  The configuration of representational units is such that the parts which do not refer to the particulars related to the respective patients, refer to the same portion of reality.

46. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Distinct but similar diagnoses #56 John’s Pneumonia #78 John’s portion of pneumococs Pneumococcal pneumonia caused by #956 Bob’s pneumonia #2087 Bob’s portion of pneumococs caused by Instance-of at t1Instance-of at t2

47. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Some motivations and consequences (3) Allows evenly clean interpretations for the wealth of ‘modified’ diagnoses: –With respect to the author of the representation: ‘nursing diagnosis’, ‘referral diagnosis’ –When created: ‘post-operative diagnosis’, ‘admitting diagnosis’, ‘final diagnosis’ –Degree of belief: ‘uncertain diagnosis’, ‘preliminary diagnosis’

48. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The Translational Medicine Ontology 48 C. Denney et.al. Creating a Translational Medicine Ontology. Nature Precedings August 2009.