2. Ontology: A Guide for the Intelligence Analyst
Barry Smith

3. Problem of ensuring sensible cooperation in a massively interdisciplinary community
concept
type
instance
model
representation
data

4. What do these mean? ‘conceptual data model’ ‘semantic knowledge model’
‘reference information model’
‘an ontology is a specification of a conceptualization’

5. help is on the way ...

6. national center for ontological research

8. ECOR Partner Institutions
Laboratory for Applied Ontology, Trento/Rome
Center for Theoretical and Applied Ontology, Turin
Foundational Ontology Group, University of Leeds
JCOR – Japanese Center for Ontological Research

9. Ontologies (tech.) Ontology (phil.)
Standardized classification systems which enable data from different sources to be combined
Ontology (phil.)
The theory of being

10. The need
strong general purpose classification hierarchies created by domain specialists
thoroughly tested in real use cases
to help us navigate through oceans of data

11. Good ontologies should be
intelligible to human beings
computationally useful
capable of being glued together

12. The actuality (too often)
myriad special purpose ‘light’ ontologies, prepared by ontology engineers and deposited in internet ‘repositories’ or ‘registries’
which only create NEW oceans of data

13. Schemaweb ontologies (http://www.w3.org/)
MusicBrainz Metadata Vocabulary
Musical Baton Vocabulary
Beer Ontology
Kissology

14. ‘Lite’ ontologies often do not generalize …
repeat work already done by others
are not gluable together
no roadmap for progressive improvement
reproduce the very problems of communication which ontology was designed to solve

15. Ontology (science)
The empirical study of how to build humanly useful and computationally tractable representations of entities and of the relations between them
Evidence-based terminology research

16. Why NCOR? Why NCOR? NCOR will advance ontology as science
develop measures of quality for ontologies to establish best practices

17. Why NCOR?
NCOR will
provide coordination and support for investigators working in ontology and its applications
engage in outreach endeavors designed to foster the goals of high quality ontology in both theory and practice
advance ontology education

18. National Center for Biomedical Ontology
$18.8 mill. NIH Roadmap Center
Stanford Medical Informatics
University of San Francisco Medical Center
Berkeley Drosophila Genome Project
Cambridge University Department of Genetics
The Mayo Clinic
University at Buffalo Department of Philosophy

19. From
chromosome
to disease

20. … legacy of Human Genome Project
genomics
transcriptomics
proteomics
reactomics
metabonomics
phenomics
behavioromics
connectomics
toxicopharmacogenomics
bibliomics
… legacy of Human Genome Project

22.  need for semantic annotation of data
where in the body ?
what kind of
disease process ?
 need for semantic annotation of data
dir.niehs.nih.gov/ microarray/datamining/

23. Woops: 54M already ! Compare with 3M Dec 2004, and 12 M june 2005 when I did this.

25. natural language labels
to make the data cognitively
accessible to human beings
dir.niehs.nih.gov/ microarray/datamining/

26. compare: legends for maps

27. ontologies are legends for data
dir.niehs.nih.gov/ microarray/datamining/

28. compare: legends for cartoons

29. legends
help human beings use and understand complex representations of reality
help human beings create useful complex representations of reality
help computers process complex representations of reality

30. computationally tractable legends
help human beings find things in very large complex representations of reality

32. ontologies are legends for images

33. what lesion ?
what brain function ?

34. which period?
which architectural style?
which type of building?

35. ontologies are legends for mathematical equations
xi = vector of measurements of gene i
k = the state of the gene ( as “on” or “off”)
θi = set of parameters of the Gaussian model
...

36. ontologies are legends for word lists
...and the Computer's View
name
education CV
private
work
© 2006 Adam Pease, Articulate Software
Slide inspired by Frank von Harmelan
Slide inspired by Frank von Harmelan

37. The Idea GlyProt MouseEcotope DiabetInGene GluChem sphingolipid
transporter
activity
DiabetInGene
GluChem

38. annotation using common ontologies yields integration of databases
MouseEcotope
GlyProt
Holliday junction
helicase complex
DiabetInGene
GluChem

39. Glue-ability / integration
rests on the existence of a common benchmark called ‘reality’
the ontologies we want to glue together are representations of what exists in the world
not of what exists in the heads of different groups of people

40. truth is correspondence to reality

41. simple representations can be true

42. there are true cartoons

43. a cartoon can be a veridical representation of reality

44. a network diagram can be a veridical representation of reality

47. pathway maps are representations of complexes of types

48. maps may be correct by reflecting topology, rather than geometry

49. an image can be a veridical
representation of reality
a labeled image can be a more
useful veridical representation
of reality

50. an image labelled with
computationally tractable labels can
be an even more useful veridical representation
of reality

51. annotations help us to find images

52. annotations using common ontologies can yield integration of image data

53. and link image databases together
Gazetteer
GlyProt
ruins of
Hadrami mosque
CIA Factbook
GluChem

54. if you’re going to semantically annotate piles of data, better work out how to do it right from the start

55. two kinds of annotations

56. names of types

57. names of instances

58. instances vs. types
dir.niehs.nih.gov/ microarray/datamining/

59. instances vs. types
types
dir.niehs.nih.gov/ microarray/datamining/

60. instances

61. molecular images and radiographic images are representations of instances

62. First basic distinction
type vs. instance
(science text vs. diary)
(human being vs. Tom Cruise)

63. For ontologies
it is generalizations that are important = ontologies are about types, kinds

64. Inventory vs. Catalog: Two kinds of representational artifact
Databases represent instances
Ontologies represent types

65. Catalog vs. inventory A 515287 DC3300 Dust Collector Fan B 521683
Gilmer Belt C
521682
Motor Drive Belt

66. Catalog vs. inventory

67. Catalog of types/Types

68. Ontology types Instances

69. Ontology = A Representation of types

70. An ontology is a representation of types
We learn about types in reality from looking at the results of scientific experiments in the form of scientific theories
experiments relate to what is particular science describes what is general

71. object
types
organism
animal
cat
mammal
siamese
frog
instances

72. Ontologies are here

73. or here

74. ontologies represent general structures in reality (leg)

75. Ontologies do not represent concepts in people’s heads

76. They represent types in reality

77. which provide the benchmark for integration

78. My job here Not tools: Leo Obrst, Chris Welty
Not instances: Werner Ceusters
Ontology content : the types in reality

79. How to build an ontology
create an initial top-level classification of your domain = ~50 most common types of entities
arrange the corresponding expressions terms into an informal is_a hierarchy according to this universality principle
A is_a B  every instance of A is an instance of B
fill in missing terms to give a complete hierarchy
move on to populate the lower levels of the hierarchy)
annotate your data

80. Example domain: threat, vulnerability
Eric Little

81. Example domain: The ontology of documents
Hernando de Soto

82. valuable work on ‘documents’ in the context of XML, etc.
e.g. Bob Glushko: “A document  is a purposeful and self-contained collection of information.”
focuses on information content,
but there is more than information here

83. transactional documents
passport
contract
tax form
bill of lading
shipping authorization
plane ticket
visa

84. the legal powers of documents
the social interactions in which they play a role
the institutional systems to which they belong
provenance (original, copy, forgery ...)

85. document vs. attachments
signatures, seals, stamps ...

87. anchoring documents to reality

88. Countersignatures

89. document template vs. filled-in document
document vs. the piece of paper (or other physical carrier) upon which it is written/printed, ...

91. Standardized documents
filled in
completely/partially
correctly/incorrectly
validly/invalidly

92. from the Shiprock Navajo fair New Mexico, September 30-October 1, 2005

93. Standardized documents
allow networking
across time
across space (individuals linked through document systems)
improve the flow of communications
allow standardized transactions

94. Documents are artifacts
analogous to
organizations, rules, prices, debts, claims and obligations ...

95. John Searle The Construction of Social Reality
claims and obligations are brought into existence by the performance of speech acts

96. appointings, marryings, promisings
change the world
We perform a speech act ... the world changes, instantaneously

97. The de Soto thesis
document systems are mechanisms for creating the institutional orders of modern societies

98. stock and share certificates create capital
marriage licenses create bonds of matrimony
statutes of incorporation create companies
title deeds create property rights and property owners
insurance certificates create insurance coverage

99. Identity documents create identity
and thereby create the possibility of identity theft
what is the ontology of identity?
what is the epistemology of identity (the technologies of identification)?

100. What you can do with a document
sign it
stamp it
witness it
fill it in
revise it
nullify it
deliver it (de facto, de jure)
...

101. types of document systems
types of document acts
types of document systems
types of document pathways

102. if you’re going to semantically annotate piles of data, better work out how to do it right from the start

103. Tomorrow: The problems, and a strategy for the future