1. The World as Database Barry Smith University at Buffalo Institute for Formal Ontology and Medical Information Science, University of Leipzig http://ontologist.com

2. The riddle of representation two humans, a monkey, and a robot are looking at a piece of cheese; what is common to the representational processes in their visual systems?

3. Answer: The cheese, of course

4. The Technological Background How the world became part of the World Wide Web the cheese

5. Sources “Motion in Databases: Issues and Possible Solutions” Ouri Wolfson (University of Illinois) “Intersection of GI and IT Spatial Databases” Max J. Egenhofer (University of Maine)

6. Information Technologies Global Positioning Systems (GPS)

7. Digital cameras Information Technologies

8. Digital video cameras Information Technologies

9. chemical biological Information Technologies Microsensors

10. Location based services Examples: Where is the closest gas station? How do I get there? Track my pet/child/prisoner

11. Location based services Wall Street Journal May 8, 2000: Location- based services a killer application for the wireless internet Strategy Analytics: consumer lbs a $7B market in North America by 2005 Why now? – Proliferation of portable/wearable/wireless devices

12. Moving Objects Database Technology Query example: How often is bus #5 late by more than 10 minutes at station 20? GPS Wireless link

13. Moving Objects Database Technology Trigger example: Send message when helicopter in a given geographic area (trigger) GPS Wireless link

14. Moving Objects Database Technology Query example: List trucks that will reach destination within 20 minutes (future query) GPS Wireless link

15. Moving Objects Database Technology Present query: List taxi cabs within 1 mile of my location GPS Wireless link

16. PalmPilot context aware Automatically display the resume of a person I am speaking with Display the wiring/plumbing behind this wall Display seismographic charts, maps, graphics, images, concerning a terrain a geologist is viewing

17. European Media Lab, Heidelberg Tourism information services Intelligent, speaking camera plus map display Display all non-smoking restaurants within walking distance of the castle Read out a history of the building my camera is pointing to

18. Mobile e-commerce Inform a person located at L who needs items of a given sort where he can them (a) most quickly (b) most cheaply (c) at 2am. Inform a person walking past a bar of his buddies in the bar

19. Further Applications Digital battlefield Emergency response Air traffic control Supply chain management Mobile workforce management Dynamic allocation of bandwidth in cellular network

20. Syntax and Semantics

21. Traditional Syntactic/Semantic Approach to Information Systems 011011 101010 00100 010001 001001 001001 00100 010011 110010 010110 1111011 011101 1

22. String-Arrays vs. Objects ghjui123 xxxxx

23. Fodor’s Methodological Solipsism 011011 101010 00100 010001 001001 001001 00100 010011 110010 010110 1111011 011101 1

24. Humans, Machines, and the Structure of Knowledge Harry M. Collins SEHR, 4: 2 (1995)

25. Knowledge-down-a-wire Imagine a 5-stone weakling having his brain loaded with the knowledge of a champion tennis player. He goes to serve in his first match -- Wham! -- his arm falls off. He just doesn't have the bone structure or muscular development to serve that hard.

26. Sometimes it is the world which knows

27. I know where the book is = I know how to find it I know what the square root of 2489 is = I know how to calculate it I know how to recognize the presencfe of a tiger = Smell, noise … (in real-world context)

28. A. Clark, Being There humans can accomplish much without building detailed, internal models; we rely on Epistemic action = manipulating Scrabble tiles – using the re-arranged pieces as basis for brain's pattern-completing abilities writing one large number above another to multiply them with pen on paper and on External scaffolding = maps, models, tools, language, culture we act so as to simplify cognitive tasks by "leaning on" the structures in our environment.

29. Not all calculations done inside the head Gibson: the world is not all chaos the information outside of the head (the environment) is structured in a way that the brain can process

30. Types of knowledge/ability/skill 1.those that can be transferred simply by passing signals from one brain/computer to another. 2.those that can’t: -- here the "hardware" is important (a) abilities/skills contained in the body (b) abilities/skills contained in the world

31. From The Methodological Solipsist Approach to Information Processing To The Ecological Approach to Information Processing … J. J. Gibson

32. Functioning of Information System intelligible only as part of environment 01101 11010 1000 1000 1000 1001 0010 0100 1001 0001 00111 1001 0010 11011 11011 01110 11

33. Ontology

34. … 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

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

36. 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)

37. Aristotle First ontologist

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

39. Linnaean Ontology

40. 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

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

42. 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 Information Systems ?

43. Each information system 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 information systems (sciences) need to be merged?

44. The Information System Tower of Babel Problem

46. Opportunities Sensor-based information systems Massively parallel data acquisition location per second of each person SIG-INT and HUM-INT

47. Result: The World Wide Web Vast amount of heterogeneous data sources Needs dramatically better support for richly structured ontologies in databases Ability to query and integrate across different ontologies (Semantic Web)

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

49. 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

50. 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

51. SO FAR SO GOOD

52. 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 (methodological solipsism …)

54. Quine

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

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

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

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

59. 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

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

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

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

63. 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.

64. 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

66. Why, then, do information systems ontologists study peoples’ beliefs, thoughts, concepts (STRING-ARRAYS) rather than the objects themselves?

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

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

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

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

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

73. They turn to substitutes: to models, to conceptualizations to STRING-ARRAYS because these are easier to handle

74. 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

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

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

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

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

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

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

82. Can we do better? Test Domain: Medical Terminology

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

84. 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

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

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

87. 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

88. Blood

89. 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

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

91. So what is the ontology of blood?

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

93. concept systems may be simply incommensurable

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

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

96.  problem of ‘merging’ ontologies “golem” “phantasy”

97. Another Example: Statements of Accounts Company Financial statements may be prepared under either the (US) GAAP or the (European) IASC standards Under the two standards, cost items are often allocated to different revenue and expenditure categories depending on the tax laws and accounting rules of the countries involved.

98. Ontology’s job is to develop an algorithm for the automatic conversion of income statements and balance sheets between the two systems. Not even this relatively simple problem has been satisfactorily resolved … why not?

99. because the two concept systems are simply incommensurable

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

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

102. 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 it must relate not to beliefs, concepts, syntactic strings but to the world itself

103. 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

104. 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 …

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

106. 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

107. Ontology like cartography must work with maps at different scales

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

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

110. 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

111. 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 golems

112. The End