Introduction to ontologies in computer science.

1. human
person
ontologies and on the web
in computer science
fabien, gandon, inria

2. book victor hugo
2

3. what is the
balance
of the project ?
3

4. 4

5. 5

6. one word, two
meanings
6

7. do not read
the following sign
7

8. too late
8

9. we interpret
machines don't
9

10. The Man Who Mistook His Wife for a Hat :
And Other Clinical Tales by Oliver W. Sacks
In his most extraordinary book, quot;one of the great clinical writers of the 20th centuryquot; (The New
York Times) recounts the case histories of patients lost in the bizarre, apparently inescapable world
of neurological disorders. Oliver Sacks's The Man Who Mistook His Wife for a Hat tells the stories
of individuals afflicted with fantastic perceptual and intellectual aberrations: patients who have
lost their memories and with them the greater part of their pasts; who are no longer able to
recognize people and common objects; who are stricken with violent tics and grimaces or who
shout involuntary obscenities; whose limbs have become alien; who have been dismissed as
retarded yet are gifted with uncanny artistic or mathematical talents.
If inconceivably strange, these brilliant tales remain, in Dr. Sacks's splendid and sympathetic telling, deeply human. They
are studies of life struggling against incredible adversity, and they enable us to enter the world of the neurologically
impaired, to imagine with our hearts what it must be to live and feel as they do. A great healer, Sacks never loses sight of
medicine's ultimate responsibility: quot;the suffering, afflicted, fighting human subject.quot;
Our rating :
Oliver Sacks
Find other books in : Neurology Psychology
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10

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11

12. something is missing
some knowledge
12

13. what is the last
document
that you read ?
13

14. documents
{ }
14

15. your answer is based on a
shared ontology
you can reason
I can understand
15

16. kind
of
Document
Book
Novel Short story
16

17. kind
of
quot;documentquot; #12
quot;bookquot; #21
quot;livrequot;
#47 #48
quot;novelquot; quot;short storyquot;
quot;romanquot; quot;nouvellequot;
17

18. #12
#21 ⇒ #12
#21
#47 ⇒ #21 #48 ⇒ #21
#47 #48
formalized ontological knowledge 18

19. ontology
is not a synonym of
taxonomy
19

20. taxonomical
knowledge is a kind of
ontological
knowledge among others
20

21. part
of
CH4 C2 H6 CH3-OH C2H6-OH
…
methane ethane methanol ethanol
CO2 O3 -OH H2
-CH3 O2 H2 O
ozone
carbon dioxide dioxygen phenol water dihydrogen
methyl
C O H
carbon oxygen hydrogen
21

22. combine
different kinds of ontological knowledge
Organic object
Individual Limb
Cat
Hierarchical model of the shape of the human body. D. Marr and H.K. Nishihara, Representation and recognition
of the spatial organization of three-dimensional shapes, Proc. R. Soc. London B 200, 1978, 269-294).
22

23. ontos
to be / beings
“Jacob Lorhard's quot;Ogdoas Scholasticaquot; (1606) contains the first occurrence of
Ogdoas
the term ‘ontologia’ ” Raul Corazzon on formalontology.it
logos
discourse/science
23

24. Ontology ontology
->
24

25. ntology
O
a logical theory which gives an explicit,
partial account of a conceptualization i.e. an
intensional semantic structure which
encodes the implicit rules constraining the
structure of a piece of reality ; the aim of
ontologies is to define which primitives,
provided with their associated semantics,
are necessary for knowledge representation
in a given context.
[Gruber, 1993] [Guarino & Giaretta, 1995] [Bachimont, 2000]
25

26. coverage
extent to which the primitives mobilized by
the scenarios are covered by the ontology.
26

27. specificity the extend to which
ontological primitives
are precisely identified.
27

28. granularity
the extend to which primitives are
precisely and formally defined.
28

29. formality
the extend to which primitives are
described in a formal language.
29

30. ontology
knowledge-based
system
30

31. e.g. students have marks
s s
marks are floats ≤ 20 and ≥ 0
s
31

32. ontology
knowledge-based
system
32

33. e.g. Stephan had a mark of 15.5
33

34. knowledge base
ontology
knowledge-based
system
rules
34

35. e.g. if a student has at least one mark
below 8 then he fails the year
35

36. knowledge base
ontology
knowledge-based
system
rules verification
36

37. e.g. the total number of marks for a course
must be equal to the total number of
students attending the course
37

38. knowledge base
ontology
knowledge-based
system
rules verification explanation
etc.
38

39. languages
to formalize ontologies
39

40. (define-class human (?human)
:def (animal ?human))
example
40 subsumption in frames

41. (defprimconcept MALE)
(defprimconcept FEMALE)
(disjoint MALE FEMALE)
example
41 disjoint classes in description logics

42. [Concept: Director]->(Def)->
[LambdaExpression:
[Person: λ] ->(Manage) -> [Group]]
example
42 defined class in conceptual graphs

43. W3C®
43

44. RDF is a triple model i.e. every
piece of knowledge is broken down into
( subject , predicate , object )
44

45. doc.html has for author Fabien
and has for theme Music
45

46. ( doc.html , author , Fabien )
( doc.html , theme , Music )
( subject , predicate , object )
RDF triples
46

47. Fabien
author
doc.html
theme
Music
RDF graphs
47

48. <rdf:RDF
xmlns:rdf=quot;http://www.w3.org/1999/02/22-
rdf-syntax-ns#quot;
xmlns:inria=quot;http://inria.fr/schema#quot; >
<rdf:Description
rdf:about=quot;http://inria.fr/rr/doc.htmlquot;>
<inria:author rdf:resource=
quot;http://inria.fr/~fabien#mequot; />
<inria:theme>Music</inria:theme>
</rdf:Description>
</rdf:RDF>
RDF XML syntax 48

49. RDFS provides primitives for
S
lightweight ontologies
49

50. <Class rdf:ID=quot;Manquot;>
<subClassOf rdf:resource=quot;#Personquot;/>
<subClassOf rdf:resource=quot;#Malequot;/>
<label xml:lang=quot;enquot;>man</label>
<comment xml:lang=quot;enquot;>an adult male
person</comment>
</Class>
example
50
a class declaration in RDFS

51. OWL
51

52. <owl:Class rdf:ID=quot;Manquot;>
<owl:intersectionOf rdf:parseType=quot;Collectionquot;>
<owl:Class rdf:about=quot;#Malequot;/>
<owl:Class rdf:about=quot;#Personquot;/>
</owl:intersectionOf>
</owl:Class>
example
52 intersection of classes in OWL

53. specify meaning
with unique identifiers
< >…</ >
53

54. link
to the world
54

55. you are here
tens of billions
of triples already online, RDF is flying (e.g. http://sindice.com/ )
55

56. Life
cycle
Design
Needs Evolution Diffusion
Manage
Evaluate Use
56

57. motivating scenarios, competency
needsquestions,
Design
Needs Evolution Diffusion
Manage
Evaluate Use
57

58. design knowledge acquisition
techniques, natural language processing,
formalisms formal concept analysis,
methodologies & intermediary representations
Design
Needs Evolution Diffusion
Manage
Evaluate Use
58

59. diffusion identify, publish,
advertise, web, peer-to-peer and other networks,
standards (e.g., OWL)
Design
Needs Evolution Diffusion
Manage
Evaluate Use
59

60. use in daily applications, in daily tasks
(find, monitor, combine, analyze, reuse, suggest
etc.), inferences, interfaces.
Design
Needs Evolution Diffusion
Manage
Evaluate Use
60

61. evaluate c.f. needs + trace and
usage analysis, metrics from methods,
collective dimension and consensus
Design
Needs Evolution Diffusion
Manage
Evaluate Use
61

62. evolution alignment,
c.f. design + versioning, version
coherence checking and all dependencies
Design
Needs Evolution Diffusion
Manage
Evaluate Use
62

63. manage as any project,
complete methodologies
Design
Needs Evolution Diffusion
Manage
Evaluate Use
63

64. the domain trap
the application domain may be
different from the ontology domain
64

65. I never saw a universal
ontology
65

66. methods
e.g. rigidity in Onto Clean [Guarino & Welty]
Rigid φ+R φ is a necessary property for all its instances
Anti-Rigid φ~R φ is an optional property for all its instances
Constraint: φ~R can't subsume ψ+R
Person is ψ+R, Student is φ~R
66

67. holistic
knowledge, but
finite ontologies
67

68. building block
vs.
changing block
68

69. ontology-based
doesn’t mean you need
an inference engine
69

70. SSRSSLSSS
SSLSSLSSS
SSL
world-wide
errors Berry
inspired by Gérard
70

71. acquisition & evolution
bottlenecks
71

72. tagging
and other web 2.0 practices
72

73. 73

74. a tag
a data attached to an object
origins of geometry
74

75. social tagging
collaboratively creating and
managing tags to annotate and
categorize content.
75

76. folks onomy
the mass of users to organize the mass of data
76

77. f olksonomy
folks~taxonomy, a subject indexing systems
created within internet communities. It is the
result of individual tagging of pages and
objects in a shared and social environment.
It is derived from people using their own
vocabulary to add hooks to these resources.
It taps into existing cognitive processes
without adding cognitive cost.
[Vander Wal, 2005] [Vander Wal, 2007][Rashmi Sinha, 2005]
77

78. folksonomies
are not the opposite of
ontologies
78

79. folksonomies
can be seen as a new
way to build and maintain
ontologies
79

80. many tags
for many uses
cool
to compare with RR176
origins of geometry
send to Ted
絕對虛假
for the SysDev team
;-)
80

81. many societies
my bookmarked page
socially shared bookmark
bookmark shared across
people an applications
81

82. ontologies
folksonomies
82

83. example
learning applications
83

84. describe…
users, learning objects,
curriculums,
84

85. e.g. LOM (Learning Object Metadata)
has nine types of characteristics:
general, life-cycle, meta-metadata,
technical, educational, rights, relations,
annotation, classification
85

86. scenario
S ?
knowledge transfer/(re)use/analysis?
evaluation/test/marking?
profiling/customizing?
feedback/curriculum management?
86

87. Dublin core
Creative Commons
FOAF …
87

88. take-home
summary and messages
88

89. web”
“semantic
and not
“semantic web” [C. Welty, ISWC 2007]
89

90. a lightweight ontology
allows us to do
lightweight reasoning
[J. Hendler, ISWC 2007]
90

91. you can’t
foresee
each and every
use and reuse
91

92. avoid building another
black box
92

93. make conceptualizations
explicit
93

94. open your data
to anyone who might use it
94

95. just my…
95

96. fabien, gandon, inria
http://ns.inria.fr/fabien.gandon
http://www.slideshare.net/fabien_gandon/
96