1. 1 Ontology as Master Discipline of Information Science Barry Smith http://ifomis.de

2. 2 Ontology as Master Discipline of Information Science Barry Smith http://ifomis.de Real

3. 3 Real Ontology is a branch of philosophy the science of what is the science of the kinds and structures of objects, properties, events, processes and relations in reality

4. 4 Real ontology seeks to provide a definitive and exhaustive classification of entities in all spheres of being.

5. 5 It seeks to answer questions like this: What categories of entities are needed for a complete description and explanation of all the goings- on in the universe?

6. 6 Ontology is in many respects comparable to the theories produced by science … but it is radically more general than these

7. 7 It can be regarded as a kind of generalized chemistry or zoology (Aristotle’s ontology grew out of biological classification) (Russell: Logic is a zoology of facts)

8. 8 Aristotle First ontologist

9. 9 First ontology ( from Porphyry’s Commentary on Aristotle’s Categories)

10. 10 Linnaean Ontology

11. 11 Ontologies are (very roughly) taxonomical trees

12. 12 Ontology is distinguished from the special sciences it seeks to study all of the various types of entities existing at all levels of granularity

13. 13 and to establish how they hang together to form a single whole (‘reality’ or ‘being’)

14. 14 Unity achieved via a good theory of relations and via taxonomies at different levels of granularity (atomic, molecular, cellular, organismic, etc.)

15. 15 Sources for ontological theorizing: thought experiments the study of ancient texts development of formal theories the results of natural science now also: working with computers

16. 16 The existence of computers and of large databases allows us to express old philosophical problems in a new light

17. 17 The problem of the unity of science The logical positivist solution to this problem addressed a world in which sciences are identified with printed texts What if sciences are identified with Large Databases ?

18. 18 Each family of databases has its own idiosyncratic terms and concepts by means of which it represents the information it receives How to resolve the incompatibilities which result when databases need to be merged?

19. 19 The Database Tower of Babel Problem

20. 20

21. 21 The term ‘ontology’ came to be used by information scientists to describe the construction of standardized taxonomies designed to make databases mutually compatible and thus to make data transportable from one environment to another

22. 22 An ‘ontology’ is a dictionary of terms formulated in a canonical syntax and with commonly accepted definitions and axioms designed to yield a shared framework for use by different information systems communities.

23. 23 An ontology is a concise and unambiguous description of the principal, relevant entities of an application domain and of their potential relations to each other

24. 24 SO FAR SO GOOD

25. 25 But how was this idea in fact realized? How did information systems engineers proceed to build ontologies? By looking at the world, surely Well, No They built ontologies by looking at what people think about the world

26. 26

27. 27 Quine

28. 28 For Quineans Ontology studies, not reality, but scientific theories From ontology … to ontological commitment

29. 29 Quine: each natural science has its own preferred repertoire of types of objects to the existence of which it is committed

30. 30 Quineanism: ontology is the study of the ontological commitments or presuppositions embodied in the different natural sciences

31. 31 Quine: only natural sciences can be taken ontologically seriously The way to do ontology is exclusively through the investigation of scientific theories

32. 32 Thus it is reasonable to identify ontology – the search for answers to the question: what exists? – with the study of the ontological commitments of natural scientists All natural sciences are compatible with each other

33. 33 PROBLEM The Quinean view of ontology becomes strikingly less defensible when the ontological commitments of various non-scientists are allowed into the mix

34. 34 How, ontologically, are we to treat the commitments of astrologists, clairvoyants, believers in voodoo?

35. 35 How, ontologically, are we to treat the commitments of patients who believe that their illness is caused by evil spirits or magic spells?

36. 36 Growth of Quinean ontology outside philosophy: Psychologists and cognitive anthropologists have sought to elicit the ontological commitments (‘ontologies’, in the plural) of different cultures and groups.

37. 37 This is not ontology Not all the things that people believe in are genuine objects of ontological investigation Only what exists is a genuine object of ontological investigation

38. 38

39. 39 Why, then, do information systems ontologists study peoples’ beliefs, thoughts, concepts rather than the objects themselves?

40. 40 Arguments for Ontology as Conceptual Modeling Ontology is hard. Life is short. Let’s do conceptual modeling instead

41. 41 programming real ontology into computers is hard therefore: we will simplify ontology and not care about reality at all

42. 42 Painting the Emperor´s Palace is h a r d

43. 43 therefore we will not try to paint the Palace at all... we will be satisfied instead with a grainy snapshot of some other building

44. 44

45. 45 Ontological engineers neglect the standard of truth to reality in favor of other, putatively more practical, standards: above all programmability

46. 46 They turn to substitutes: to models, to conceptualizations because these are easier to handle (… they move from messy noumenal reality to neatly packaged “phenomena” …)

47. 47 For an information system ontology there is no reality other than the one created through the system itself, so that the system is, by definition, correct

48. 48 Only those objects exist which are represented in the system (constructivism)

49. 49 Tom Gruber (1995): ‘For AI systems what “exists” is what can be represented’

50. 50 Ontological engineering concerns itself with conceptualizations It does not care whether these are true of some independently existing reality.

51. 51 In the world of information systems there are many surrogate world models and thus many ontologies

52. 52 … and all ontologies, are equal both good and bad,

53. 53 ATTEMPTS TO SOLVE THE TOWER OF BABEL PROBLEM VIA ONTOLOGIES AS “CONCEPTUAL MODELS” HAVE FAILED

54. 54 HALF WAY THROUGH

55. 55 Can Real Ontology do Better? Test Domain: Medical Terminology

56. 56 Example 1: UMLS Universal Medical Language System Taxonomy system maintained by National Library of Medicine in Washington DC 134 semantic types 800,000 concepts 10 million inter-concept relationships

57. 57 Example 2: SNOMED Systematized Nomenclature of Medicine Taxonomy system maintained by the College of American Pathologists 121,000 concepts 340,000 relationships

58. 58 SNOMED designed to foster interoperability to serve as a “common reference point for comparison and aggregation of data throughout the entire healthcare process”

59. 59 Problems with UMLS and SNOMED Each is a fusion of several source vocabularies They were fused without an ontological system being established first  They contain circularities, taxonomic gaps, unnatural ad hoc determinations … several billion dollars still being wasted in the making of retrospective fixes

60. 60 Example 3: GALEN Two levels: ontologically powerful model of clinical information inside the computer plus a range of terminological services for clinical tasks involving different coding systems, including natural language

61. 61 Problems with GALEN Ontology is ramshackle and has been subject to repeated fixes Its unnaturalness makes coding slow and expensive Coding thus far limited in extent (surgical processes)

62. 62 Blood

63. 63 Representation of Blood in UMLS Blood Tissue Entity Physical Object Anatomical Structure Fully Formed Anatomical Structure An aggregation of similarly specialized cells and the associated intercellular substance. Tissues are relatively non-localized in comparison to body parts, organs or organ components Body SubstanceBody FluidSoft Tissue Blood as tissue

64. 64 Representation of Blood in SNOMED Blood Liquid Substance Substance categorized by physical state Body fluid Body Substance Substance Blood as fluid

65. 65 Representation of Blood in GALEN Blood SoftTissue DomainCategory Phenomenon Blood as SoftTissue with two states: LiquidBlood and CoagulatedBlood Substance Tissue GeneralisedSubstanceSubstanceorPhysicalStructure

66. 66 Plus attempts at Patients’ Ontology based on WordNet = online lexical reference system for the English language

67. 67 Representation of Blood in WordNet Blood Humor the four fluids in the body whose balance was believed to determine our emotional and physical state along with phlegm, yellow and black bile Entity Physical Object Substance Body Substance Body Fluid Blood as humor

68. 68 So what is the ontology of blood?

69. 69 We cannot solve this problem just by looking at concepts

70. 70 concept systems may be simply incommensurable

71. 71 the problem can only be solved by taking the world itself into account

72. 72 This implies a view of ontology not as a theory of concepts but as a theory of reality But how is this possible? How can we get beyond our concepts? answer: ontology must be maximally opportunistic

73. 73 Maximally opportunistic means: look at concepts and beliefs critically and always in the context of a wider view which includes independent ways to access the objects themselves at different levels of granularity

74. 74 Ontology must be maximally opportunistic This means: don’t just look at beliefs look at the objects themselves from every possible direction, formal and informal scientific and non-scientific …

75. 75 Maximally opportunistic means: look at the same objects at different levels of granularity:

76. 76 “ skritek ” objects are in the world not all concepts correspond to objects not all concepts are relevant to ontology concepts are in the head

77. 77  problem of ‘merging’ ontologies “skritek” “blaznivy”

78. 78 How to solve the Tower of Babel Problem? How to fuse these mutually incompatible ‘conceptual models’ of blood ? By drawing on the results of philosophical work in ontology carried out over the last 2000 years

79. 79 First Step: A good medical domain ontology presupposes a good formal or top- level ontology

80. 80 Formal part hole connected (spatially, causally) substance system state

81. 81 Material organism tissue symptom circulatory system organ is the nose an organ? is the circulatory system an organ

82. 82 Second step: select out the good conceptualizations these have a reasonable chance of being integrated together into a single ontological system based on tested principles robust conform to natural science

83. 83 IFOMIS Institute for Formal Ontology and Medical Information Science University of Leipzig

84. 84 PARTNERS Ontology Group, LADSEB-CNR, Padua/Trento Institute of Cognitive Sciences and Technologies (ISTC-CNR), Rome ONTEK Corporation Language and Computing, Belgium www.landc.be

85. 85 Strategy: A Network of Domain Ontologies Material (Regional) Ontologies Basic Formal Ontology (BFO)

86. 86 A Network of Domain Ontologies BFO

87. 87 A Network of Domain Ontologies BFO B(Chem)O

88. 88 A Network of Domain Ontologies BFO B(Chem)OB(Med)O

89. 89 A Network of Domain Ontologies BFO B(Chem)OB(Med)OB(Cell)O

90. 90 A Network of Domain Ontologies BFO B(Chem)OB(Med)OB(Cell)OB(Gen)O

91. 91 A Network of Domain Ontologies BFO B(Chem)OB(Med)OB(Cell)OB(Gen)OB(Epidem)O

92. 92 Ontology like cartography must work with maps at different scales and with maps picking out different dimensions of invariants

93. 93

94. 94 Thus ontology needs

95. 95 Ontological Zooming

96. 96 Universe/Periodic Table animal bird canary ostrich fish ontology of biological species ontology of DNA space

97. 97 Universe/Periodic Table animal bird canary ostrich fish both are transparent partitions of one and the same reality

98. 98 There are many compatible map-like partitions many maps at different scales, all transparent to the reality beyond the mistake arises when one supposes that only one of these partitions is a true map of what exists

99. 99 Medical ontologies at different levels of granularity: cell ontology drug ontology * protein ontology gene ontology * anatomical ontology * epidemiological ontology

100. 100 Medical ontologies disease ontology therapy ontology pathology ontology * and also physician’s ontology patient’s ontology

101. 101 Medical ontologies and even hospital management (billing) ontology * * = already exists (but in a variety of mutually incompatible forms)

102. 102 Partitions should be cuts through reality a good medical ontology should NOT be compatible with the conceptualization of disease as: caused by evil spirits and demons and cured by skritek

103. 103 IFOMIS Tests Uniform top-level ontology for medicine applicable at distinct granularities Test-case development of partial medical domain ontologies applied to: Standardization of clinical trial protocols Clinical trial Merkmal-dictionary Processing of unstructured patient records (www.landc.be)

104. 104 Uniform top-level ontology for medicine ONE YEAR Applicable at distinct granularities (e.g. gene ontology) FOUR YEARS Standardization of clinical trial protocol TWO YEARS Clinical trial Merkmal-dictionary TWO YEARS Processing of unstructured patient records (www.landc.be) THREE YEARS

105. 105 Uniform top-level ontology for medicine NO COMPETITOR applicable at distinct granularities NO COMPETITOR Standardization of clinical trial protocol NO SERIOUS COMPETITOR Clinical trial Merkmal-dictionary NO COMPETITOR Processing of unstructured patient records MANY COMPETITORS, BUT GOOD MEASURES OF EFFECTIVENESS

106. 106 The End