Introductory lecture to the VU University Amsterdam Master course on Ontology Engineering. See http://semanticweb.cs.vu.nl/OE2012/

1. Welcome
to
Ontology
Engineering
Guus
Schreiber

2. Lecture
1
Agenda
• Course
introduc:on:
what
is
an
ontology?
• Administra:on
• RDF/RDFS

3. Literature
• James
Odell,
Ontology
White
Paper,
CSC
Catalyst,
2011,
V2011-­‐07-­‐15,
hNp://www.jamesodell.com/
Ontology_White_Paper_2011-­‐07-­‐15.pdf.
• For
this
lecture
Sec.s
1-­‐4
are
relevant
• Acknowledgement:
some
figures
in
this
lecture
come
from
the
paper
above.

4. What
is
an
Ontology?
• In
philosophy:
theory
of
what
exists
in
the
world
• In
IT:
consensual
&
formal
descrip:on
of
shared
concepts
in
a
domain
• Aid
to
human
communica:on
and
shared
understanding,
by
specifying
meaning
• Machine-­‐processable
(e.g.,
agents
use
ontologies
in
communica:on)
• Key
technology
in
seman:c
informa:on
processing
• Applica:ons:
knowledge
management,
e-­‐business,
seman:c
world-­‐wide
web.

5. What
is
an
Ontology?
II
“explicit
specifica-on
of
a
shared
conceptualiza-on
that
holds
in
a
par-cular
context”
(several
authors)

6. Knowledge
sharing
and
reuse
• Knowledge
engineering
is
costly
and
:me-­‐
consuming
• Distributed
systems
• Increasing
need
for
defini:on
of
a
common
frame
of
reference
– Internet
search,
document
indexing,
….

7. Need
for
data
integra:on?

8. Seman:c
Web
• Data
integra:on
• AAA
slogan
• Non-­‐Unique
Naming
Assump:on
• Open
vs.
closed
World

9. The
Web:
resources
and
links
Web
link
URL
URL

10. The
Seman:c
Web:
typed
resources
and
links
Pain:ng
Dublin
Core
ULAN
“Woman
with
hat
SFMOMA
creator
Henri
Ma:sse
Web
link
URL
URL

11. Seman:c
Web

12. WordNet

14. Domain
standards
and
vocabularies
as
ontologies
• Contain
ontological
informa:on
• Ontology
needs
to
be
“extracted”
– Not
explicit
• Lists
of
domain
terms
are
some:mes
also
called
“ontologies”
– Implies
a
weaker
no:on
of
ontology
– Scope
typically
much
broader
than
a
specific
applica:on
domain
– Contain
some
meta
informa:on:
hyponyms,
synonyms,
text
• Structured
knowledge
is
available
(on
the
web)
–
use
it!
14

15. Ontology
spectrum
Source: http://www.jamesodell.com/Ontology_White_Paper_2011-07-15.pdf.

16. Document
fragment
ontologies
16

17. Instruc:onal
document
fragment
ontologies
17

18. Context
and
Domain
Principle
1:
“ The
representa:on
of
real-­‐world
objects
always
depends
on
the
context
in
which
the
object
is
used.
This
context
can
be
seen
as
a
“viewpoint”
taken
on
the
object.
It
is
usually
impossible
to
enumerate
in
advance
all
the
possible
useful
viewpoints
on
(a
class
of
)
objects.”
Principle
2:
“Reuse
of
some
piece
of
informa:on
requires
an
explicit
descrip:on
of
the
viewpoints
that
are
inherently
present
in
the
informa:on.
Otherwise,
there
is
no
way
of
knowing
whether,
and
why
this
piece
of
informa:on
is
applicable
in
a
new
applica:on
seing.”

19. Mul:ple
views
on
a
domain
• typical
viewpoints
captured
in
ontologies:
• func:on
• behavior,
• causality
• shape,
geometry
• structure:
part-­‐of
(mereology),
aggrega:on
• connectedness
(topology)
• viewpoints
can
have
different
abstrac:on
(generaliza:on)
levels
• viewpoints
can
overlap
• applica:ons
require
combina:ons
of
viewpoints
19

20. Mul:ple
views
on
a
domain
20

21. Context
specifica:on
through
ontology
types
• Domain-­‐specific
ontologies
– Medicine:
UMLS,
SNOMED,
Galen
– Art
history:
AAT,
ULAN
– STEP
applica:on
protocols
• Task-­‐specific
ontologies
– Classifica:on
– E-­‐commerce
• Generic
ontologies
– Top-­‐level
categories
– Units
and
dimensions
21

22. Top-­‐level
categories:
many
different
proposals
Chandrasekaran et al. (1999)
22

23. The
famous
is-­‐a
rela:onship
Source: http://www.jamesodell.com/Ontology_White_Paper_2011-07-15.pdf.

24. Classes
as
instances
24
Source: http://www.jamesodell.com/Ontology_White_Paper_2011-07-15.pdf.

25. What
is
an
Ontology?
“explicit
specifica-on
of
a
shared
conceptualiza-on
that
holds
in
a
par-cular
context”
(several
authors)

26. Concepts
• Help
us
organize
the
world
around
us
• Act
as
recogni:on
device
• Test
for
reality
• We
use
many
different
types
of
concepts

27. Concept
types
Source: http://www.jamesodell.com/Ontology_White_Paper_2011-07-15.pdf.

28. The
concept
triad
Source: http://www.jamesodell.com/Ontology_White_Paper_2011-07-15.pdf.

29. Concept
specifica:on
• Symbol
– Name
used
for
the
concept
– Can
be
different
names,
different
languages
– E.g.,
“bike”,
fiets”
• Intension
(defini:on)
– Intended
meaning
of
the
concept
(seman:cs)
– E.g.
a
bike
has
at
least
one
wheel
and
a
human-­‐
powered
movement
mechanism
• Extension
– Set
of
examples
of
the
concept
– E.g.
“my
bike”,
“your
bike”

30. Incomplete
concept
specifica:ons
• Are
common
• Think
of
an
example:
– Concept
with
no
instances
– Concept
with
no
symbol
• Primi:ve
vs.
defined
concepts

31. Domain
=
area
of
interest
• Can
be
any
size
– e.g.,
medicine
• Concepts
may
have
different
symbols
in
different
domains
• The
same
symbol
may
be
used
for
different
concepts
in
different
domains
(some:mes
also
in
the
same
domain)

32. Ontology
Specifica:on
• Class
(concept)
• Aggrega:on
• Subclass
with
inheritance
• Rela:on-­‐aNribute
dis:nc:on
• Trea:ng
rela:ons
as
classes
• Rela:on
(slot)
• Sloppy
class/instance
dis:nc:on
– Class-­‐level
aNributes/
rela:ons
– Meta
classes
• Constraints
• Data
types
• Modularity
– Import/export
of
an
ontology
• Ontology
mapping

33. Ontology
Languages
– UML
– RDF
Schema,
OWL
– …..
• Common
basis
– Class
(concept)
– Subclass
with
inheritance
– Rela:on
(slot)
33

34. Ontology
Tools
Best
known
tool
• Protégé
(Stanford)
• We
will
use
this
tool
Decision
points:
– Expressivity
– Graphical
representa:on
– DB
backend
– Modulariza:on
support
– Versioning

35. Administra:on
• Course
website:
hNp://seman:cweb.cs.vu.nl/OE2012/
• Use
blog
posts
for
content
ques:ons
• Use
oe-­‐list@few.vu.nl
for
admin
ques:ons

36. Engineering
needs
prac:ce!
Lots
of
exercises
throughout
the
course:
• Two
mee:ngs
per
week
• Lectures
on
Monday
• Work
sessions
on
Thursday
• You
are
encouraged
to
do
assignments
together
with
colleagues
• Individual
porsolio

37. RDF(S)
Recap
• Which
RDF/RDF-­‐Schema
constructs
do
you
remember?

38. URIs,
URLs
• URI:
global
iden:fier
for
a
web
resource
• hNp://www.w3.org/2006/03/wn/wn20/instances/synset-­‐
anniversary-­‐noun-­‐1
• URL:
dereferencable
URI,
used
to
locate
a
file
on
the
web.
• hNp://www.w3.org/2006/03/wn/wn20/instances/synset-­‐
anniversary-­‐noun-­‐1
• URI
abbrevia:ons:
– Qnames
• Namespace:iden:fier
• Wordnet:synset-­‐anniversary-­‐noun-­‐1

39. Triples
ulan:Shakespeare ulan:parentOf ulan:Susanna.
kb:Hamlet kb:author kb:Shakspeare.
ex:VrijeUniversiteit ex:locatedIn tgn:Amsterdam.
ex:WillemHage ex:teaches ex:OntologyEngineering.
ex:OntologyEngineering rdf:type ex:Course.

40. Syntax
• N3
Turtle
– hNp://www.w3.org/TeamSubmission/turtle/
• RDFXML
– hNp://www.w3.org/TR/rdf-­‐syntax-­‐grammar/

41. Blank
nodes
How
would
you
model
“Sonnet78
was
inspired
by
a
woman
who
lives
in
England”?
Lit:Sonnet78 lit:hasInspiration [ rdf:type bio:Woman;
bio:livedIn geo:England ] .

42. subClassOf
IF
A rdfs:subClassOf B
r rdf:type A
THEN
r rdf:type B

43. subPropertyOf
IF
P rdfs:subPropertyOf R
a P b
THEN
a R b

44. Domain
and
Range
IF
IF
P rdfs:domain D
P rdfs:range R
x P y
x P y
THEN
THEN
x rdf:type D
y rdf:type R

45. More
RDF(S)
• rdfs:label
• rdfs:comment
• rdfs:seeAlso

46. RDF-­‐Schema
• Provides
a
way
to
talk
about
the
vocabulary
– Define
classes,
proper:es
bb:author rdf:type rdfs:Property
• Enables
inferencing
– Inferring
new
triples
from
asserted
triples.
• subClassOf,
subPropertyOf,
domain,
range.

47. Guidelines
for
ontological
engineering
(1)
• Do
not
develop
from
scratch
• Use
exis:ng
data
models
and
domain
standards
as
star:ng
point
• Start
with
construc:ng
an
ontology
of
common
concepts
• If
many
data
models,
start
with
two
typical
ones
• Make
the
purpose
and
context
of
the
ontology
explicit
– E.g.
data
exchange
between
ship
designers
and
assessors
– Opera:onally
purpose/context
with
use
cases
• Use
mul:ple
hierarchies
to
express
different
viewpoints
on
classes
• Consider
trea:ng
central
rela:onships
as
classes
47

48. Guidelines
for
ontological
engineering
(2)
• Do
not
confuse
terms
and
concepts
• Small
ontologies
are
fine,
as
long
as
they
meet
their
goal
• Don’t
be
overly
ambi:ous:
complete
unified
models
are
difficult
• Ontologies
represent
sta:c
aspects
of
a
domain
– Do
not
include
work
flow
• Use
a
standard
representa:on
format,
preferably
with
a
possibility
for
graphical
representa:on
• Decide
about
the
abstrac:on
level
of
the
ontology
early
on
in
the
process.
– E.g.,
ontology
only
as
meta
model
48