This document provides an introduction to ontology engineering, focusing on definitions, principles, and applications of ontologies in various domains. It emphasizes the importance of shared conceptualizations, knowledge sharing, and data integration, alongside the use of ontology languages and tools. Guidelines for developing ontologies are also outlined, stressing the significance of context, viewpoint, and existing data models.

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