1. 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 Tutorial Realism-Based Ontology for Integrating Individually Compiled Biomedical Data Repositories August 26, 2012 – Palazzo dei Congressi, Pisa, Italy Werner CEUSTERS, MD Center of Excellence in Bioinformatics and Life Sciences, Ontology Research Group and Department of Psychiatry University at Buffalo, NY, USA http://www.org.buffalo.edu/RTU

2. 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 Short personal history 1959 - 2012 1977 1989 1992 1998 2002 2004 2006 1993 1995

3. 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 Tutorial overview The problems of terminology-based ontologies Principles of Ontological Realism From model-theoretic languages to reference-based languages Overview of OBO Foundry ontologies Integrating clinical datasets about orofacial pain 3

4. 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 Why this tutorial, this way ? 4

5. 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 To avoid this: 5 Figure shamelessly stolen without permission from X*, 2009. * source undisclosed to avoid embarrassment of its authors.

6. 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 … and this: 6 Figure shamelessly stolen without permission from X*, 2009. * source undisclosed to avoid embarrassment of its authors.

7. 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 Two abundantly present fundamental mistakes (1) You can’t exchange mental illnesses through websites or have Protégé interact with illnesses;  = confusing information with what information is about! 7

8. 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 It happens sometimes to experts 8 Schulz S, Brochhausen M, Hoehndorf R. Higgs Bosons, Mars Missions, and Unicorn Delusions: How to Deal with Terms of Dubious Reference in Scientific Ontologies. In: Smith B, Proc. of the International Conference on Biomedical Ontologies. Buffalo NY,2011. p 188.

9. 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 Two abundantly present fundamental mistakes (2) Psychological views are as much special kinds of associated problems as associated problems are special kinds of bipolar disorder;  = using ontology tools while ignoring the underlying semantics. 9

10. 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 10 I hope this is a joke http://www.mkbergman.com/http://www.mkbergman.com/ Last accessed: Jan 31, 2012 reproduction licensed through: http://creativecommons.org/licenses/by-nc-sa/2.5/

11. 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 Part 1 The problems of terminology-based ontologies 11

12. 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 Language is ambiguous ‘I know that you believe that you understood what you think I said, but I am not sure you realize that what you heard is not what I meant.’ –Robert McCloskey, State Department spokesman (attributed). http://www.quotationspage.com/quotes/Robert_McCloskey/

13. 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 Language is ambiguous Often we can figure it out … in Miami hotel lobby warning on plastic bag in Miami bar

14. 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 Language is ambiguous in Amsterdam hotel elevator Sometimes, we can not …

15. 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 It is worse for machines... “John Doe has a pyogenic granuloma of the left thumb” We see:     The machine sees:

16. 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 It is worse for machines... John Doe pyogenic granuloma of the left thumb The XML misunderstanding We see:    The machine sees:

17. 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 The clever (?) business man and his XML card John Nitwit 524 Moon base avenue Utopia … Is this the name of the business card or of the business card owner?

18. 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 Intermediate conclusion We need for sure methods and techniques that allow: –people to express exactly what they mean, –people to understand exactly what is communicated to them, –machines to communicate information without any distortion. If information overload is a problem, we also need methods and techniques that allow machines to understand exactly what is communicated to them.

19. 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 Unfortunately … Traditional terminology alone is not going to do the job, Not even when you express it (naively) in OWL !!! (yes, I am shouting) 19

20. 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 But what the word ‘concept’ denotes, is usually not clarified and users of it often refer to different entities in a haphazard way: Why not: most terminologies are ‘concept’-based Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, Biomedical Ontology in Action, November 8, 2006, Baltimore MD, USA

21. 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 SNOMED about diseases and concepts (until 2010) ‘Disorders are concepts in which there is an explicit or implicit pathological process causing a state of disease which tends to exist for a significant length of time under ordinary circumstances.’ And also: “Concepts are unique units of thought”. Thus: Disorders are unique units of thoughts in which there is a pathological process …??? And thus: to eradicate all diseases in the world at once we simply should stop thinking ?

22. 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 But what the word ‘concept’ denotes, is usually not clarified and users of it often refer to different entities in a haphazard way: meaning shared in common by synonymous terms idea shared in common in the minds of those who use these terms unit of describing meanings knowledge universal that what is shared by all and only all entities in reality of a similar sort Most terminologies are ‘concept’-based Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, Biomedical Ontology in Action, November 8, 2006, Baltimore MD, USA

23. 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 But what the word ‘concept’ denotes, is usually not clarified and users of it often refer to different entities in a haphazard way, the result being: chaos Most terminologies are ‘concept’-based Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, Biomedical Ontology in Action, November 8, 2006, Baltimore MD, USA

24. 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 Some examples 24

25. 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 25 Border’s classification of medicine: what’s wrong ? Medicine –Mental health –Internal medicine Endocrinology –Oversized endocrinology Gastro-enterology... –Pediatrics –... –Oversized medicine Refer to the size of the books that do not fit on a normal Border’s Bookshop shelf

26. 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 26 Is this a good idea ? Cover subject matter of papers Cover the form of papers

27. 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 27 Principle A representation should not mix object language and meta language –object language describes the referents in the subject domain –meta language describes the object language

28. 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 28 Geographic Locations: a good hierarchy ? Africa [Z01.058] + Americas [Z01.107] + Antarctic Regions [Z01.158] Arctic Regions [Z01.208] Asia [Z01.252] + Atlantic Islands [Z01.295] + Australia [Z01.338] + Cities [Z01.433] + Europe [Z01.542] + Historical Geographic Locations [Z01.586] + Indian Ocean Islands [Z01.600] + Oceania [Z01.678] + Oceans and Seas [Z01.756] + Pacific Islands [Z01.782] + mereological mess mixture of geographic entities with socio- political entities mixture of space and time

29. 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 29 Geographic Locations [Z01] Africa [Z01.058] + Americas [Z01.107] + Antarctic Regions [Z01.158] Arctic Regions [Z01.208] Asia [Z01.252] + Atlantic Islands [Z01.295] + Australia [Z01.338] + Cities [Z01.433] + Europe [Z01.542] + Historical Geographic Locations [Z01.586] + Indian Ocean Islands [Z01.600] + Oceania [Z01.678] + Oceans and Seas [Z01.756] + Pacific Islands [Z01.782] + Ancient Lands [Z01.586.035] + Austria-Hungary [Z01.586.117] Commonwealth of Independent States [Z01.586.200] + Czechoslovakia [Z01.586.250] + European Union [Z01.586.300] Germany [Z01.586.315] + Korea [Z01.586.407] Middle East [Z01.586.500] + New Guinea [Z01.586.650] Ottoman Empire [Z01.586.687] Prussia [Z01.586.725] Russia (Pre-1917) [Z01.586.800] USSR [Z01.586.950] + Yugoslavia [Z01.586.980] +

30. 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 30 Geographic Locations [Z01] Africa [Z01.058] + Americas [Z01.107] + Antarctic Regions [Z01.158] Arctic Regions [Z01.208] Asia [Z01.252] + Atlantic Islands [Z01.295] + Australia [Z01.338] + Cities [Z01.433] + Europe [Z01.542] + Historical Geographic Locations [Z01.586] + Indian Ocean Islands [Z01.600] + Oceania [Z01.678] + Oceans and Seas [Z01.756] + Pacific Islands [Z01.782] + Ancient Lands [Z01.586.035] + Austria-Hungary [Z01.586.117] Commonwealth of Independent States [Z01.586.200] + Czechoslovakia [Z01.586.250] + European Union [Z01.586.300] Germany [Z01.586.315] + Korea [Z01.586.407] Middle East [Z01.586.500] + New Guinea [Z01.586.650] Ottoman Empire [Z01.586.687] Prussia [Z01.586.725] Russia (Pre-1917) [Z01.586.800] USSR [Z01.586.950] + Yugoslavia [Z01.586.980] +

31. 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 31 Principle A hierarchical structure should not represent distinct hierarchical relations unless they are formally characterized

32. 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 32 Diabetes Mellitus in MeSH 2008 ? Different set of more specific terms when different path from the top is taken. 2

33. 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 33 MeSH: some paths from top to Wolfram Syndrome Wolfram Syndrome All MeSH Categories Diseases Category Nervous System Diseases Cranial Nerve Diseases Optic Nerve Diseases Optic Atrophy Optic Atrophies, Hereditary Neurodegenerative Diseases Heredodegenerative Disorders, Nervous System Eye Diseases Eye Diseases, Hereditary Optic Nerve Diseases Male Urogenital Diseases Urologic Diseases Kidney Diseases Diabetes Insipidus Female Urogenital Diseases and Pregnancy Complications Female Urogenital Diseases

34. 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 34 What would it mean if used in the context of a patient ? Wolfram Syndrome All MeSH Categories Diseases Category Nervous System Diseases Cranial Nerve Diseases Optic Nerve Diseases Optic Atrophy Optic Atrophies, Hereditary has Neurodegenerative Diseases Heredodegenerative Disorders, Nervous System Eye Diseases Eye Diseases, Hereditary Optic Nerve Diseases Female Urogenital Diseases and Pregnancy Complications Female Urogenital Diseases Male Urogenital Diseases Urologic Diseases Kidney Diseases Diabetes Insipidus ??? … has

35. 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 35 Principle If a particular (individual) is related in a specific way to a ‘class’, it should also be related in the same way to all the ‘superclasses’ of that class –Technically: “… to all the classes that subsume that class”

36. 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 36 Body Regions [A01] –Extremities [A01.378] Lower Extremity [A01.378.610] –Buttocks [A01.378.610.100] –Foot [A01.378.610.250] »Ankle [A01.378.610.250.149] »Forefoot, Human [A01.378.610.250.300] + »Heel [A01.378.610.250.510] –Hip [A01.378.610.400] –Knee [A01.378.610.450] –Leg [A01.378.610.500] –Thigh [A01.378.610.750] What’s wrong ? MeSH Tree Structures – 2007

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 37 SNOMED-CT: abundance of false synonymy nose bones fracture

38. 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 38 Coding / Classification confusion A patient with a fractured nasal bone A patient with a broken nose A patient with a fracture of the nose = =

39. 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 39 A patient with a fractured nasal bone A patient with a broken nose A patient with a fracture of the nose = = Coding / Classification confusion A patient with a fractured nasal bone A patient with a broken nose A patient with a fracture of the nose = =

40. 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 Summary of Part 1 40

41. 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 41 Summary of current deficiencies in traditional and formal terminologies (1) Terms often require “reading in context”, agrammatical constructions (paper-based indexing), semantic drift as one moves between hierarchies, not (yet) useful for natural language understanding by software (but were not designed for that purpose),

42. 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 42 Summary of current deficiencies in traditional and formal terminologies (2) labels for terms do not correspond with formal meaning, underspecification (leading to erroneous classification in DL-based systems), overspecification (leading to wrong assumptions with respect to instances).

43. 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 43 Take-home message Concept-based terminology (and standardisation thereof) is there as a mechanism to improve understanding of messages by humans. It is NOT the right device –to explain why reality is what it is, how it is organised, etc., (although it is needed to allow communication), –to reason about reality, –to make machines understand what is real, –to integrate across different views, languages, conceptualisations,...

44. 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 Principles of Ontological Realism 44

45. 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’ In philosophy: –Ontology (no plural) is the study of what entities exist and how they relate to each other; –by some philosophers taken to be synonymous with ‘metaphysics’ while others draw distinctions in many distinct ways (the distinctions being irrelevant for this talk), but almost agreeing on the following classification: metaphysics –general metaphysics »ontology –special metaphysics –distinct from ‘epistemology’ which is the study of how we can come to know about what exists.

46. 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 A legitimate ontological question Do mental illnesses / disorders / diseases exist? –The answer can, arguably, be ‘no’: if one does not subscribe to the mind-brain dichotomy: –no mind  nothing mental if one does, but also entertains a strong body-related interpretation of what is an illness, disorder, disease: –STEDMAN (27th edition): an interruption, cessation, or disorder of body function, system, or organ. –DORLAND: any deviation from or interruption of the normal structure or function of a part, organ, or system of the body as manifested by characteristic symptoms and signs; –WHO: an interconnected set of one or more dysfunctions in one or more body parts, linking to underling genetic factors and to interacting environmental factors and possibly: to a pattern or patterns of response to interventions. –and under the same conditions: ‘yes’: ‘mental disorder’ would be synonymous with ‘brain disorder’

47. 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 A legitimate ontological question Do mental illnesses / disorders / diseases exist? –The answer can, arguably, be ‘no’: if one does not subscribe to the mind-brain dichotomy: –no mind  nothing mental if one does, but also entertains a strong body-related interpretation of what is an illness, disorder, disease: –STEDMAN (27th edition): an interruption, cessation, or disorder of body function, system, or organ. –DORLAND: any deviation from or interruption of the normal structure or function of a part, organ, or system of the body as manifested by characteristic symptoms and signs; –WHO: an interconnected set of one or more dysfunctions in one or more body parts, linking to underling genetic factors and to interacting environmental factors and possibly: to a pattern or patterns of response to interventions. –and under the same conditions: ‘yes’: ‘mental disorder’ would be synonymous with ‘brain disorder’

48. 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 A better phrased ontological question Do mental illnesses / disorders / diseases exist? What, if anything at all, do the terms ‘mental illness’, ‘mental disorder’, … denote?

49. 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 A better phrased ontological question Do mental illnesses / disorders / diseases exist? What, if anything at all, do the terms ‘mental illness’, ‘mental disorder’, … denote? termsfirst-order reality ‘mental disorder’ ‘person’ ‘UB’

50. 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 This is distinct from terminological questions Terminological question: –What does ‘mental disorder’ mean ?

51. 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 This is distinct from terminological questions Terminological question: –What does ‘mental disorder’ mean ? ‘mental disorder’ meaning of ‘mental disorder’

52. 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 Terminological approaches raise further questions Terminological question: –What does ‘mental disorder’ mean ? ‘mental disorder’ meaning of ‘mental disorder’ What are meanings? Do meanings denote? How are these related?

53. 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 The half-baked semantic/semiotic triangle does not provide any good answers despite its overwhelming popularity ‘mental disorder’ meaning of ‘mental disorder’ Concept Symbol / Sign / TermThing / Referent

54. 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 The semantic triangle works sometimes fine term concept referent ‘Beethoven’ Ludwig van Beethoven that great German composer that became deaf …

55. 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 The semantic triangle works sometimes fine term concept referent ‘Beethoven's Symphony No. 3’ Beethoven’s symphony dedicated to Bonaparte the symphony played after the Munich Olympics massacre … ‘Beethoven's Opus 55’ ‘Eroica’

56. 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 Sometimes the semantic triangle fails term concept referent ‘Beethoven's Symphony No. 11’ the symphony Beethoven wrote after the tenth …

57. 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 Sometimes the semantic triangle fails term concept referent ‘Beethoven's Symphony No. 11’ the symphony Beethoven wrote after the tenth … some hold this term has meaning

58. 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 Sometimes the semantic triangle fails term concept referent ‘Beethoven's Symphony No. 10’ the one assembled by Barry Cooper from fragmentary sketches Beethoven’s hypothetical symphony …

59. 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 Prehistoric ‘psychiatry’: drapetomania term concept referent ‘drapetomania’ disease which causes slaves to suffer from an unexplainable propensity to run away … painting by Eastman Johnson. A Ride for Liberty: The Fugitive Slaves. 1860.

60. 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 The questions the triangle raises become trickier Is … –Beethoven’s 10 th symphony a symphony ? –Beethoven’s 10 th symphony a hypothetical symphony ? –a hypothetical symphony a symphony ? In medicine, is … –a prevented abortion an abortion ? –an absent nipple a nipple ?

61. 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 should give the answer In philosophy: –Ontology (no plural) is the study of what entities exist and how they relate to each other;

62. 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 Unfortunately, ‘ontology’ denotes ambiguously 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;

63. 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 as it should be done 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; The realist view within the Ontology Research Group combines the two: –We use Ontological Realism, a specific methodology that uses ontology as the basis for building high quality ontologies, using reality as benchmark.

64. 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 64 The Terminology / Ontology divide Terminology: –solves certain issues related to language use, i.e. with respect to how we talk about entities in reality (if any); Relations between terms / concepts –does not provide an adequate means to represent independent of use what we talk about, i.e. how reality is structured; Women, Fire and Dangerous Things (Lakoff). Ontology (of the right sort) : –Language and perception neutral view on reality. Relations between entities in first-order reality This is the ‘terminology / ontology divide’

65. 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 65 Why not ? Does not take care of universals and particulars appropriately Concepts not necessarily correspond to something that (will) exist(ed) –Sorcerer, unicorn, leprechaun,... Definitions set the conditions under which terms may be used, and may not be abused as conditions an entity must satisfy to be what it is Language can make strings of words look as if it were terms –“Middle lobe of left lung”...

66. 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

67. 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 1.There is an external reality which is ‘objectively’ the way it is; 2.That reality is accessible to us; 3.We build in our brains cognitive representations of reality; 4.We communicate with others about what is there, and what we believe there is there. The basis of Ontological Realism Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, Biomedical Ontology in Action, November 8, 2006, Baltimore MD, USA

68. 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 Ontological Realism makes three crucial distinctions 1.Between data and what data are about; 2.Between continuants and occurrents; 3.Between what is generic and what is specific. 68 Smith B, Ceusters W. Ontological Realism as a Methodology for Coordinated Evolution of Scientific Ontologies. Applied Ontology, 2010.

69. 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 69

70. 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 L1 - L2 L3 70 Linguistic representations about (1), (2) or (3) Clinicians’ beliefs about (1) Entities (particular or generic) with objective existence which are not about anything Representations First Order Reality

71. 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 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 71

72. 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 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 72

73. 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 Ontological Realism makes crucial distinctions Between data and what data are about; Between continuants and occurrents: –obvious differences: a person versus his life a disease versus its course space versus time –more subtle differences: observation (data-element) versus observing diagnosis versus making a diagnosis message versus transmitting a message 73

74. 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 Is depression considered a continuant or occurrent? What do we mean by ‘depression’ ? –The name of some disease ?  continuant –A bout of feelings of being worth nothing, sobbing, appearance of suicidal thoughts, …  occurrent

75. 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 Between ‘generic’ and ‘specific’ L1. First-order reality L2. Beliefs (knowledge) GenericSpecific DIAGNOSIS INDICATION my doctor’s work plan my doctor’s diagnosis MOLECULE PERSON DISEASE PATHOLOGICAL STRUCTURE PORTION OF INSULIN DRUG me my blood glucose level my NIDDM my doctor my doctor’s computer L3. Representation ‘person’‘drug’‘insulin’‘W. Ceusters’‘my sugar’ Referent TrackingBasic Formal Ontology GenericSpecific 75

76. 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 From model-theoretic languages to reference-based languages 76

77. 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 Model theoretic semantics (1) Meaning of traditional logic is based on model theoretic semantics which defines meaning in terms of a model (a.k.a. possible world), a set-theoretic structure that defines a (potentially infinite) set of objects with properties and relations between them. A model connects language and the world by representing the abstract objects and relations that exist in a possible world. An interpretation is a mapping from logic to the model that defines predicates extensionally, in terms of the set of tuples of objects that make them true (their extension). –The extension of Red(x) is the set of all red things in the world. –The extension of Father(x,y) is the set of all pairs of objects such that A is B’s father. 77

78. 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 Model theoretic semantics (2) Model theoretic semantics gives the truth conditions for a sentence, i.e. a model satisfies a logical sentence iff the sentence evaluates to true in the given model. The meaning of a sentence is therefore defined as the set of all possible worlds in which it is true. 78

79. 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 However … Generic terms used to denote specific entities do not have enough referential capacity –Usually enough to convey that some specific entity is denoted, –Not enough to be clear about which one in particular. For many ‘important’ entities, unique identifiers are used: –UPS parcels –Patients in hospitals –VINs on cars –…

80. 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 explicit reference to the concrete individual entities relevant to the accurate description of some portion of reality,... Fundamental goals of ‘our’ Referent Tracking Ceusters W, Smith B. Strategies for Referent Tracking in Electronic Health Records. J Biomed Inform. 2006 Jun;39(3):362-78.

81. 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 Method: numbers instead of words Ceusters W, Smith B. Strategies for Referent Tracking in Electronic Health Records. J Biomed Inform. 2006 Jun;39(3):362-78. –Introduce an Instance Unique Identifier (IUI) for each relevant particular (individual) entity 78

82. 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 Use these identifiers in expressions using a language that acknowledges the structure of reality: e.g.: a yellow ball: then not : yellow(#1) and ball(#1) rather: #1: the ball#2: #1’s yellow Then still not: ball(#1) and yellow(#2) and hascolor(#1, #2) but rather: instance-of(#1, ball, since t1) instance-of(#2, yellow, since t2) inheres-in(#1, #2, since t2) Fundamental goals of ‘our’ Referent Tracking  Strong foundations in realism-based ontology

83. 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 The shift envisioned From: –‘this man is a 40 year old patient with a stomach tumor’ To (something like) : –‘this-1 on which depend this-2 and this-3 has this-4’, where this-1 instanceOf human being … this-2 instanceOf age-of-40-years … this-2 qualityOf this-1 … this-3 instanceOf patient-role … this-3 roleOf this-1 … this-4 instanceOf tumor … this-4 partOf this-5 … this-5 instanceOf stomach … this-5 partOf this-1 … …

84. 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 The shift envisioned From: –‘this man is a 40 year old patient with a stomach tumor’ To (something like) : –‘this-1 on which depend this-2 and this-3 has this-4’, where this-1 instanceOf human being … this-2 instanceOf age-of-40-years … this-2 qualityOf this-1 … this-3 instanceOf patient-role … this-3 roleOf this-1 … this-4 instanceOf tumor … this-4 partOf this-5 … this-5 instanceOf stomach … this-5 partOf this-1 … … denotators for particulars

85. 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 The shift envisioned From: –‘this man is a 40 year old patient with a stomach tumor’ To (something like) : –‘this-1 on which depend this-2 and this-3 has this-4’, where this-1 instanceOf human being … this-2 instanceOf age-of-40-years … this-2 qualityOf this-1 … this-3 instanceOf patient-role … this-3 roleOf this-1 … this-4 instanceOf tumor … this-4 partOf this-5 … this-5 instanceOf stomach … this-5 partOf this-1 … … denotators for appropriate relations

86. 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 The shift envisioned From: –‘this man is a 40 year old patient with a stomach tumor’ To (something like) : –‘this-1 on which depend this-2 and this-3 has this-4’, where this-1 instanceOf human being … this-2 instanceOf age-of-40-years … this-2 qualityOf this-1 … this-3 instanceOf patient-role … this-3 roleOf this-1 … this-4 instanceOf tumor … this-4 partOf this-5 … this-5 instanceOf stomach … this-5 partOf this-1 … … denotators for universals or particulars

87. 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 The shift envisioned From: –‘this man is a 40 year old patient with a stomach tumor’ To (something like) : –‘this-1 on which depend this-2 and this-3 has this-4’, where this-1 instanceOf human being … this-2 instanceOf age-of-40-years … this-2 qualityOf this-1 … this-3 instanceOf patient-role … this-3 roleOf this-1 … this-4 instanceOf tumor … this-4 partOf this-5 … this-5 instanceOf stomach … this-5 partOf this-1 … … time stamp in case of continuants

88. 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 Relevance: the way RT-compatible EHRs ought to interact with representations of generic portions of reality instance-of at t #105 caused by

89. 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 Overview of OBO Foundry ontologies 89

90. 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 The OBO Foundry a family of interoperable biomedical reference ontologies built around the Gene Ontology (GO) at its core and using the same principles as the GO a modular annotation catalogue of English phrases each module created by experts from the corresponding scientific community http://obofoundry.org

91. 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

92. 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 OBO Website

93. 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 Basic Formal Ontology: an upper ontology based on Ontological Realism 93

94. 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 A useful parallel: Alberti’s grid reality representation Ontological theory

95. 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 95 Basic components of the BFO view on the world The world consists of –entities that are Either particulars or universals; Either occurrents or continuants; Either dependent or independent; and, –relationships between these entities of the form e.g. is-instance-of, lacks e.g. is-member-of, is-part-of e.g. isa (is-subtype-of) Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, November 8, 2006, Baltimore MD, USA

96. 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 96 The example to work (partially) out: ‘walking’ methis walking Has-participant at t 2 human being Instance-of at t living creature Is_a walking Instance-of my left leg part-of at t this leg moving leg moving part-of leg to make me walk function process Instance-of at t Instance-of at t Is_a Instance-of Has- Participant at t Is-realized- In at t Has-function at t

97. 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 97 The parallel with molecular phenomena this PCompthis cell’s life Has-participant at t 2 protein complex Instance-of at t molecular structure Is_a cell life Instance-of this protein part-of at t this mitosis mitosis part-of protein regulate mitosis function process Instance-of at t Instance-of at t Is_a Instance-of Has- Participant at t Is-realized- In at t Has-function at t

98. 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 98 Particulars methis walking my left leg this leg moving to make me walk Individual entities that carry identity and preserve their identity over time 1

99. 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 99 Universals human being living creature walkingleg moving leg function process Entities which exist “in” the particulars amongst which there is a relation of similarity not found with other particulars 1

100. 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 100 Particulars versus Universals some particular some universal instanceOf … entities on either site cannot ‘cross’ this boundary every particular is an instance of at least one universal for every universal there is or has been at least one instance

101. 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 101 Particulars and Universals methis walking my left leg this leg moving to make me walk human being living creature walkingleg moving leg function process Instance-of at t Instance-of at t Instance-of at t Instance-of 1

102. 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 102 instanceOf at t 2 instanceOf at t 1 instanceOf at t 2 The importance of temporal indexing this-1’s stomach benign tumor instanceOf at t 1 this-4 malignant tumor partOf at t 1 stomach partOf at t 2

103. 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 103 Continuants and Occurrents methis walking my left leg this leg moving to make me walk human being living creature walkingleg moving leg function process Instance-of at t Instance-of at t Instance-of at t Instance-of 2

104. 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 104 Continuants me human being Instance-of at t my left leg leg to make me walk function Instance-of at t Instance-of at t Continuants are entities which endure (=continue to exist) while undergoing different sorts of changes, including changes of place. While they exist, they exist “in total”. 2

105. 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 105 Continuants preserve identity while changing caterpillarbutterfly animal t human being living creature me child Instance-of in 1960 adult me Instance-of since 1980 2

106. 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 106 Occurrents this walking walking Instance-of this leg moving leg moving Instance-of Occurrents are changes. Occurrents unfold themselves during temporal phases. At any point in time, they exist only in part. 2

107. 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 107 Independent versus dependent methis walking human being Instance-of at t living creature Is_a walking Instance-of my left leg this leg moving leg moving leg to make me walk function process Instance-of at t Instance-of at t Is_a Instance-of 3

108. 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 108 Independent versus dependent Independent entities Do not require any other entity to exist to enable their own existence Dependent entities Require the existence of another entity for their existence methis walking my left leg this leg moving to make me walk 3

109. 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 109 Independent versus dependent Independent entities Do not require any other entity to exist to enable their own existence Dependent entities Require the existence of another entity for their existence methis walking my left leg this leg moving to make me walk Independent continuants Dependent continuants Occurrents (are all dependent) 3

110. 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 110 Dependent continuants Realized –Quality:redness (of blood) Realizable –Function:to flex (of knee joint) –Role:student –Power:boss –Disposition:brittleness (of a bone) 3

111. 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 111 Dependent continuants Realized –Quality:redness (of blood) Realizable –Function:to flex (of knee joint) –Role:student –Power:boss –Disposition:brittleness (of a bone) Realizations flexing studying ordering breaking continuantsoccurrents 3

112. 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 112 Unconstrained reasoning OWL-DL reasoning Sorts of relations U1U2 P1 P2 UtoU: isa, partOf, … PtoU: instanceOf, lacks, denotes… PtoP: partOf, denotes, subclassOf,…

113. 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 113 Part-of different for continuants and occurrents methis walking human being Instance-of at t living creature Is_a walking Instance-of my left leg this leg moving leg moving leg process Instance-of at t Is_a Instance-of part-of at t part-of

114. 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 114 Part-of can be generalized, … with care ! me human being Instance-of at t living creature Is_a my left leg part-of at t leg Instance-of at t C part_of C1 = [def] for all c, t, if Cct then there is some c1 such that C1c1t and c part_of c1 at t. Cct = c instance-of C at t

115. 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 115 Part-of can be generalized, … with care ! me human being Instance-of at t living creature Is_a my left leg part-of at t leg Instance-of at t C part_of C1 = [def] for all c, t, if Cct then there is some c1 such that C1c1t and c part_of c1 at t. Cct = c instance-of C at t Part-of ?

116. 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 116 Part-of can be generalized, … with care ! me human being Instance-of at t living creature Is_a my left leg part-of at t leg Instance-of at t Horse legs are not parts of human beings Amputated legs are not parts of human beings ‘Canonical leg is part of canonical human being’, but…, there are (very likely) no such particulars … Part-of ?

117. 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 117 tt t instanceOf The essential pieces material object spacetime region me some temporal region my life my 4D STR some spatial region history spatial region temporal region dependent continuant some quality located-in at t … at t participantOf at toccupies projectsOn projectsOn at t

118. 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 Towards BFO 2.0: continuants 118

119. 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 Towards BFO 2.0: occurrents 119

120. 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 Not easy to understand by conceptualists (1) ‘How can cells or viruses be entirely independent entities, even within a controlled laboratory environment?’  shows not understanding what ‘ontological dependence’ means 120 Maojo V, Crespo J, Garcia-Remesal M, de la Igleasia D, Perez-Rey D, Kulikowski C. Biomedical Ontologies: Towards Scientific Debate. Methods Inf Med. 2011 March 21;50(3)

121. 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 Not easy to understand by conceptualists (2) ‘the distinction between continuants and occurrents does not account for the contrast between reversible [processes] and irreversible processes in biology, chemistry, computation, or quantum mechanics’,  compare with: the distinction between males and females does not account for the contrast between nuns and housewives. 121 Maojo V, Crespo J, Garcia-Remesal M, de la Igleasia D, Perez-Rey D, Kulikowski C. Biomedical Ontologies: Towards Scientific Debate. Methods Inf Med. 2011 March 21;50(3)

122. 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 122 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

123. 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

124. 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 To be a consistent, logical and extensible framework (ontology) for the representation of –features of disease –clinical processes –results Goal of OGMS

125. 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 Motivation Clarity about: –disease etiology and progression –disease and the diagnostic process –phenotype and signs/symptoms

126. 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 etiological processdisorderdiseasepathological process abnormal bodily featuressigns & symptomsinterpretive processdiagnosis producesbearsrealized_in producesparticipates_inrecognized_as produces Approach http://code.google.com/p/ogms/ 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. http://www.referent-tracking.com/RTU/sendfile/?file=AMIA-0075-T2009.pdf

127. 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 No conflation of diagnosis, disease, and disorder The disorder is thereThe diagnosis is here The disease is there

128. 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 Etiological process - phenobarbitol- induced hepatic cell death –produces Disorder - necrotic liver –bears Disposition (disease) - cirrhosis –realized_in Pathological process - abnormal tissue repair with cell proliferation and fibrosis that exceed a certain threshold; hypoxia-induced cell death –produces Abnormal bodily features –recognized_as Symptoms - fatigue, anorexia Signs - jaundice, splenomegaly Symptoms & Signs –used_in Interpretive process –produces Hypothesis - rule out cirrhosis –suggests Laboratory tests –produces Test results – documentation of elevated liver enzymes in serum –used_in Interpretive process –produces Result - diagnosis that patient X has a disorder that bears the disease cirrhosis Cirrhosis - environmental exposure

129. 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 Disorder =def. – A causally linked combination of physical components that is –(a) clinically abnormal and –(b) maximal, in the sense that it is not a part of some larger such combination. Pathological Process =def. – A bodily process that is a manifestation of a disorder and is clinically abnormal. Foundational Terms (1)

130. 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 Clinically abnormal - something is clinically abnormal if: –(1) is not part of the life plan for an organism of the relevant type (unlike aging or pregnancy), –(2) is causally linked to an elevated risk either of pain or other feelings of illness, or of death or dysfunction, and –(3) is such that the elevated risk exceeds a certain threshold level.

131. 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 Disorder =def. – A causally linked combination of physical components that is (a) clinically abnormal and (b) maximal, in the sense that it is not a part of some larger such combination. Pathological Process =def. – A bodily process that is a manifestation of a disorder and is clinically abnormal. Disease =def. – A disposition (i) to undergo pathological processes that (ii) exists in an organism because of one or more disorders in that organism. Foundational Terms (2)

132. 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 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. Diagnosis

133. 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 Integrating clinical datasets about orofacial pain 133

134. 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 Acknowledgement The work described is funded in part by grant 1R01DE021917-01A1 from the National Institute of Dental and Craniofacial Research (NIDCR). The content of this presentation is solely the responsibility of the author and does not necessarily represent the official views of the NIDCR or the National Institutes of Health. 134

135. 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 Collaborators 135 Werner Ceusters – Richard Ohrbach UB (PIs) Mike T. John – Eric L. Schiffman University of Minnesota Vishar Aggarwal Manchester, UK Joanna Zakrzewska London, UK Thomas List Malmö, Sweden Rafael Benoliel Hadassah, Israel

136. 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 Background (1) July 2008, Toronto: –the International RDC/TMD Consortium Network identified a need to incorporate the RDC/TMD diagnostic taxonomy into a comprehensive orofacial pain taxonomy. April, 2009, Miami: –‘The International Consensus Workshop: Convergence on an Orofacial Pain Taxonomy’ participants decided that an adequate treatment of the ontology of pain in general, and orofacial pain in particular, together with an appropriate terminology, is mandatory to advance the state of the art in diagnosis, treatment and prevention. 136 Ohrbach R, List T, Goulet J, Svensson P. Recommendations from the International Consensus Workshop: Convergence on an Orofacial Pain Taxonomy. Journal of Oral Rehabilitation. 2010.

137. 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 Background (2) The following consecutive steps were proposed: 1.study the terminology and ontology of pain as currently defined, 2.find ways to make individual data collections more useful for international research, 3.develop an ontology for integrating knowledge and data over all the known basic and clinical science domains concerning TMD and its relationship to complex disorders, and 4.expand this ontology to cover all pain-related disorders. 137

138. 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 Potential impact Improvements anticipated: –Better assessment of quality of life and disablement in relation to pain, –Increasing the reliability and validity of the Research Diagnostic Criteria for TMD, –Better methods and tools for unambiguous data annotation and classification for pain; Expected changes in the field: –Better use of self-report assessment approaches for functional limitation and psychosocial disability related to pain, –Widespread use of an adequate terminology and ontology of disease and disease perception. 138

139. 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 Project goals to obtain better insight into: –the complexity of pain disorders, pain types as well as pain-related disablement and –its association with mental health and quality of life, to develop an ontology for this subdomain incorporating a broad array of measures consistent with a biopsychosocial perspective regarding pain, to integrate five existing datasets that broadly encompass the major types of pain in the oral and associated regions. 139

140. 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 Specific Aims 1.describe the portions of reality covered by the five datasets by means of a realism-based ontology (OPMQoL), 2.design bridging axioms required to express the data dictionaries of the datasets in terms of the OPMQoL and translate these axioms in the query languages used by the underlying databases, 3.validate OPMQoL by querying the datasets with and without using the ontology and by comparing the results in function of the clinical question identified, 4.document the development and validation approach in a way that other groups can re-use and expand OPMQoL, and use our approach in other domains. 140

141. 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 Starting point: IASP definition for ‘pain’ ‘an unpleasant sensory and emotional experience associated with actual or potential tissue damage, or described in terms of such damage’; what asserts: –a common phenomenology (‘unpleasant sensory and emotional experience’) to all instances of pain, –the recognition of three distinct subtypes of pain involving, respectively: 1.actual tissue damage, 2.what is called ‘potential tissue damage’, and 3.a description involving reference to tissue damage. 141

142. 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 Five pain-related phenomena 142 Smith B, Ceusters W, Goldberg LJ, Ohrbach R. Towards an Ontology of Pain. In: Mitsu Okada (ed.), Proceedings of the Conference on Logic and Ontology, Tokyo: Keio University Press, February 2011:23-32.

143. 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 Pain with concordant tissue damage =def.: (1) a bodily process in an organism S involving two integrated levels: (1a) activation of the nociceptive system including the pain-associated emotion-generating brain components of S, and (1b) a simultaneous sensory and aversive experience on the part of S that is (2) caused by damage to tissue located in a region R of the body of the subject S, (3) experienced by S as being caused by this damage, (4) such as to involve an aversive reaction on the part of S directed towards that which is presumed by S to be causing this damage, (5) concordant with the tissue damage on both levels (1a) and (1b), and also (6) such that the sensory experience is sufficiently intense to communicate the presence of tissue damage to the subject. 143 Smith B, Ceusters W, Goldberg LJ, Ohrbach R. Towards an Ontology of Pain. In: Mitsu Okada (ed.), Proceedings of the Conference on Logic and Ontology, Tokyo: Keio University Press, February 2011:23-32.

144. 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 Tissue damage sensing and pain generation 144 David Julius & Allan I. Basbaum. Molecular mechanisms of nociception. Nature Vol 413, 13 Sept 2001

145. 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 Current general definition for ‘pain’ (1) pain =def. a bodily process in an organism S involving two integrated levels: –(a) activation of the nociceptive system and associated emotion generating brain components of S, and –(b) a simultaneous aversive sensory and emotional experience on the part of S, –where (b) is phenomenologically similar to the sort of aversive experience involved in PCT pain. 145 Smith B, Ceusters W, Goldberg LJ, Ohrbach R. Towards an Ontology of Pain. In: Mitsu Okada (ed.), Proceedings of the Conference on Logic and Ontology, Tokyo: Keio University Press, February 2011:23-32.

146. 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 Current general definition for ‘pain’ (2) ‘phenomenologically similar’ means inter alia: –(1) that the experience is ‘of’ or is ‘targeted towards’ some region R of the body of S, so that all pain is in this sense (and however diffusely) localized; –(2) that the experience involves a dimension of unpleasantness which – as is shown by the case of pain asymbolia – is not necessarily of the sort that involves suffering or aversion on the part of the subject S. 146 Smith B, Ceusters W, Goldberg LJ, Ohrbach R. Towards an Ontology of Pain. In: Mitsu Okada (ed.), Proceedings of the Conference on Logic and Ontology, Tokyo: Keio University Press, February 2011:23-32.

147. 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 Considered datasets ‘US Dataset’ (724 patients) resulted from the NIH funded RDC/TMD Validation Project, ‘Hadassah Dataset’ (306 patients) from the Orofacial Pain Clinic at the Faculty of Dentistry, Hadassah, ‘German Dataset’ (416 patients) of patients seeking treatment for orofacial pain at the Department of Prosthodontics and Materials Sciences, University of Leipzig, ‘Swedish Dataset’of 46 consecutive Atypical Odontalgia (AO) patients recruited from 4 orofacial pain clinics in Sweden as well as data about age- and gender-matched control patients, 35 of which being painless and 41 being TMD patients, ‘UK Dataset’ (168 patients) of facial pain of non dental origin present for a minimum of three months. 147

148. 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 Challenges (1) which terms used in the domain correspond with real entities, (2) what real entities need to exist for certain signs and symptoms to manifest themselves, (3) to what degree do distinct pain disorders lead to similar types of signs and symptoms, and (4) to what extent can individual patients be suffering from distinct pain disorders at the same time, yet exhibiting manifestations that can be explained by the presence of only one particular pain disorder. 148

149. 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 Mapping assessment instrument terms, ontology and patient cases 149

150. 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

151. 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 The positive effects of appropriate mappings

152. 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 The positive effects of appropriate mappings identification of ontological relations prior to statistical correlation: –ch1 and ch4 –ch1 and ch5 –ch1 and ch2 –… Contributes to answering ‘Q4: how can we make analysis feasible’ –this method allows for data- reduction without information loss.

153. 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 Linking the instruments and other tools analyze data dictionaries, assessment instruments, study criteria and corresponding terminologies, build realism-based application ontologies to link these sources to realism-based reference ontologies.

154. 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 Example: assessing TMJ Anatomy

155. 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 Panoramic X-ray of mouth

156. 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 Radiology RDC/TMD Examination: data collection sheet

157. 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 RDC/TMD: a collaborator’s data dictionary Fieldnames in that collaborator’s data collection Allowed values for the fields

158. 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 Anybody sees something disturbing ?

159. 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 This data dictionary alone is not reliable! That these variables are about the condylar head of the TMJ is ‘lost in translation’!

160. 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 ‘meaning’ of values in data collections ‘The patient with patient identifier ‘PtID4’ is stated to have had a panoramic X-ray of the mouth which is interpreted to show subcortical sclerosis of that patient’s condylar head of the right temporomandibular joint’ 1 meaning

161. 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 Objectives of the ‘sources’ analysis Find for each value V in the data collections all possible configurations of entities (according to our best scientific understanding) for which the following can be true: – V –‘it is stated that V’ Describe these possible configurations by means of sentences from a formal language that mimic the structure of reality.

162. 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 Objectives of the ‘sources’ analysis (2) For example, –for the value stating that ‘The patient with patient identifier ‘PtID4’ has had a panoramic X-ray of the mouth which is interpreted to show subcortical sclerosis of that patient’s condylar head of the right temporomandibular joint’ to be true, –this statement must have been made, –for the statement to be true, there must have been that patient, an X-ray, etc, … –BUT! It is not necessarily true that that patient has indeed the sclerosis as diagnosed.

163. 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 Methodology 1.Formulate for each variable in the data collection a sentence explaining as accurately as possible what the variable stands for, 2.list the entities in reality that the terms in the sentence denote, 3.list recursively for all entities listed further entities that ontologically must exist for the entity under scrutiny to exist, 4.classify all entities in terms of realism-based ontologies (RBO), 5.specify all obtaining relationships between these entities, 6.outline all possible configurations of such entities for the sentence to be true.

164. 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 Step 1: formulate a statement ‘The patient with patient identifier ‘PtID4’ is stated to have had a panoramic X-ray of the mouth which is interpreted to show subcortical sclerosis of that patient’s condylar head of the right temporomandibular joint’ 1 meaning

165. 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 Step 2 (1): list the entities denoted 1(The patient) with 2(patient identifier ‘PtID4’) 3(is stated) 4(to have had) a 5(panoramic X-ray) of 6(the mouth) which 7(is interpreted) to 8(show) 9(subcortical sclerosis of 10(that patient’s condylar head of the 11(right temporomandibular joint)))’ CLASSINSTANCE IDENTIFIER personIUI-1 patient identifierIUI-2 assertionIUI-3 technically investigatingIUI-4 panoramic X-rayIUI-5 mouthIUI-6 interpretingIUI-7 seeingIUI-8 diagnosisIUI-9 condylar head of right TMJIUI-10 right TMJIUI-11 notes: colors have no meaning here, just provide easy reference, this first list can be different, any such differences being resolved in step 3

166. 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 Step 2 (2): provide directly referential descriptions CLASS INSTANCE IDENTIFIERDIRECTLY REFERENTIAL DESCRIPTIONS personIUI-1 the person to whom IUI-2 is assigned patient identifierIUI-2 the patient identifier of IUI-1 assertionIUI-3 'the patient with patient identifier PtID4 has had a panoramic X-ray of the mouth which is interpreted to show subcortical sclerosis of that patient’s right temporomandibular joint' technically investigatingIUI-4the technically investigating of IUI-6 panoramic X-rayIUI-5the panoramic X-ray that resulted from IUI-4 mouthIUI-6the mouth of IUI-1 interpretingIUI-7the interpreting of the signs exhibited by IUI-5 seeingIUI-8the seeing of IUI-5 which led to IUI-7 diagnosisIUI-9the diagnosis expressed by means of IUI-3 condylar head of right TMJIUI-10the condylar head of the right TMJ of IUI-1 right TMJIUI-11the right TMJ of IUI-1

167. 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 Step 3: identify further entities that ontologically must exist for each entity under scrutiny to exist. assigner roleIUI-12the assigner role played by the entity while it performed IUI-21 assigningIUI-21the assigning of IUI-2 to IUI-1 by the entity with role IUI-12 assertingIUI-20the asserting of IUI-3 by the entity with asserter role IUI-13 asserter roleIUI-13the asserter role played by the entity while it performed IUI-20 investigator roleIUI-14the investigator role played by the entity while it performed IUI-4 panoramic X-ray machine IUI-15the panoramic X-ray machine used for performing IUI-4 image bearerIUI-16the image bearer in which IUI-5 is concretized and that participated in IUI-8 interpreter roleIUI-17the interpreter role played by the entity while it performed IUI-7 perceptor roleIUI-18the perceptor role played by the entity while it performed IUI-8 diagnostic criteriaIUI-19the diagnostic criteria used by the entity that performed IUI-7 to come to IUI-9 study subject roleIUI-22the study subject role which inheres in IUI-1

168. 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 Step 3: some remarks interpreter role, perceptor role, … –reference to roles rather than the entity in which the roles inhere because it may be the same entity and one should not assign several IUIs to the same entity each description follows similar principles as Aristotelian definitions but is about particulars rather than universals

169. 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 Step 4: classify all entities in terms of realism-based ontologies requires more ontological and philosophical skills than domain expertise or expertise with Protégé, not just term matching CLASSHIGHER CLASS personBFO: Object patient identifierIAO: Information Content Entity assertionIAO: Information Content Entity technically investigating OBI: Assay panoramic X-rayIAO: Image mouthFMA: Mouth interpretingMFO: Assessing seeingBFO: Process diagnosisIAO: Information Content Entity condylar head of right TMJ FMA: Right condylar process of mandible right TMJFMA: Right temporomandibular joint assigner roleBFO: Role assigningBFO: Process study subject roleOBI: Study subject role

170. 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 Step 5: specify relationships between these entities For instance: –at least during the taking of the X-ray the study subject role inheres in the patient being investigated: IUI-23 inheres-in IUI-1 during t1 –the patient participates at that time in the investigation IUI-4 has-participant IUI-1 during t1 These relations need to follow the principles of the Relation Ontology. Smith B, Ceusters W, Klagges B, Koehler J, Kumar A, Lomax J, Mungall C, Neuhaus F, Rector A, Rosse C. Relations in biomedical ontologies, Genome Biology 2005, 6:R46. Relations in biomedical ontologies

171. 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 Step 6: outline all possible configurations of such entities for the sentence to be true (a one semester course on its own) Such outlines are collections of relational expressions of the sort just described, Variant configurations for the example: –perceptor and interpreter are the same or distinct human beings, –the X-ray machine is unreliable and produced artifacts which the interpreter thought to be signs motivating his diagnosis, while the patient has indeed the disorder specified by the diagnosis (the clinician was lucky) –…

172. 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 Conclusion Realism-based ontology has a lot to offer to make data collections comparable and unambiguously understandable. It is hard ! How far one needs to go depends on the purposes. –ideally: an analysis should be such that it can accommodate ALL purposes, i.e. the analysis should be independent of any purpose; distinction between reference ontologies and application ontologies.