Lecture: Ontologies and the Semantic Web

Seman&c
Analysis
in
Language
Technology

http://stp.lingﬁl.uu.se/~santinim/sais/2016/sais_2016.htm  
 
Ontologies and the Semantic Web
Marina
San(ni

san$nim@stp.lingﬁl.uu.se

Department
of
Linguis(cs
and
Philology

Uppsala
University,
Uppsala,
Sweden

Spring
2016

Acknowledgements

•  Most
slides
based
on
Harrocks
(2008).

The
Seman(c
Web
&
Ontologies
2

Outline

•  The
Seman(c
Web

•  Ontologies

The
Seman(c
Web
&
Ontologies
3

Chronology

hNp://en.wikipedia.org/wiki/
History_of_the_World_Wide_Web

•  On
August
6,
1991,Berners-‐Lee
posted
a
short
summary
of
the
World
Wide

Web
project
on
the
alt.hypertext
newsgroup,
invi(ng
collaborators.
This
date

also
marked
the
debut
of
the
Web
as
a
publicly
available
service
on
the

Internet,
although
new
users
could
only
access
it
aEer
August
23.

•  Beginning
in
2002,
new
ideas
for
sharing
and
exchanging
content
ad
hoc,

such
as
Weblogs
and
RSS,
rapidly
gained
acceptance
on
the
Web.
This
new

model
for
informa(on
exchange,
primarily
featuring
user-‐generated
and

user-‐edited
websites,
was
dubbed
Web
2.0.

•  Popularized
by
Berners-‐Lee's
book
Weaving
the
Web
(2000)
and
a
Scien(ﬁc

American
ar(cle
by
Berners-‐Lee,
James
Hendler,
and
Ora
Lassila,
the
term

•  Seman&c
Web
describes
an
evolu&on
of
the
exis&ng
Web
in

which
the
network
of
hyperlinked
human-‐readable
web

pages
is
extended
by
machine-‐readable
metadata
about

documents
and
how
they
are
related
to
each
other,

enabling
automated
agents
to
access
the
Web
more

intelligently
and
perform
tasks
on
behalf
of
users.

•  In
2006,
Berners-‐Lee
and
colleagues
stated
that

the
idea
"remains
largely
unrealized"

The
Seman(c
Web
&
Ontologies
4

Web
1.0

•  Web
1.0
is
a
retronym
referring
to
an
early
stage
of
the

World
Wide
Web's
evolu(on.

•  Some
design
elements
of
a
Web
1.0
site
include:

–  Personal
web
pages
were
common,
consis(ng
mainly
of

sta(c
pages

–  Sta(c
pages
instead
of
dynamic
HTML.

–  The
use
of
HTML
3.2-‐era
elements
such
as
Framing
(World

Wide
Web)s
and
tables
to
posi(on
and
align
elements
on
a

page

(now
we
use
css
and
frames
are
deprecated)

–  GIF
buNons...

The
Seman(c
Web
&
Ontologies
5

Web
2.0

•  Web
2.0
describes
World
Wide
Web
sites
that
use
technology

beyond
the
sta(c
pages
of
earlier
Web
sites.

•  The
key
features
of
Web
2.0
include:

–  Tagging
-‐
allows
users
to
collec(vely
classify
and
find
informa(on
(e.g.

Tagging)

–  Rich
User
Experience-‐
dynamic
content;
responsive
to
user
input

–  User
Par(cipa(on
-‐
informa(on
flows
two
ways
between
site
owner
and

site
user
by
means
of
evalua(on,
review,
and
commen(ng.

–  Site
users
add
content
for
others
to
see

–  Mass
Par(cipa(on
-‐
Universal
web
access
leads
to
differen(a(on
of

concerns
from
the
tradi(onal
internet
userbase.

–  etc.

The
Seman(c
Web
&
Ontologies
6

Web
3.0

•  “Web
3.0,
a
phrase
coined
by
John
Markoﬀ
of
the
New
York
Times
in
2006,

refers
to
a
supposed
third
genera(on
of
Internet-‐based
services
that

collec(vely
comprise
what
might
be
called
‘the
intelligent
Web’
—
such
as

those
using
seman(c
web,
microformats,
natural
language
search,
data-‐
mining,
machine
learning,
recommenda(on
agents,
and
ar(ﬁcial

intelligence
technologies
—
which
emphasize
machine-‐facilitated

understanding
of
informa(on
in
order
to
provide
a
more
produc(ve
and

intui(ve
user
experience.”

•  Web
3.0
will
be
more
connected,
open,
and
intelligent,
with
seman(c
Web

technologies,
distributed
databases,
natural
language
processing,
machine

learning,
machine
reasoning,
and
autonomous
agents.

–  hNp://lifeboat.com/ex/web.3.0

The
Seman(c
Web
&
Ontologies
7

This
has
yet
to
happen.

•  "The
Web
was
designed
as
an
informa$on
space,
with
the

goal
that
it
should
be
useful
not
only
for
human-‐human

communica(on,
but
also
that
machines
would
be
able
to

par(cipate
and
help.

•  One
of
the
major
obstacles
to
this
has
been
the
fact
that

most
informa$on
on
the
Web
is
designed
for
human

consump$on,
and
even
if
it
was
derived
from
a
database

with
well
deﬁned
meanings
(in
at
least
some
terms)
for
its

columns,
that
the
structure
of
the
data
is
not
evident
to
a

robot
browsing
the
Web.

•  Leaving
aside
the
ar(ﬁcial
intelligence
problem
of
training

machines
to
behave
like
people,
the
Seman$c
Web

approach
instead
develops
languages
for
expressing

informa$on
in
a
machine
process-‐able
form"-‐

–  Tim
Berners-‐Lee,
The
Seman&c
Web
Roadmap,
1998

–  hNp://www.w3.org/DesignIssues/Seman(c.html

The
Seman(c
Web
&
Ontologies
8

The
web:
present
and
future

Today…

•  The
web
is
rela(vely
simple:

– Hypertexts
and
hypermedia

– Access
is
engineered
via
a
combina(on
of

keyword-‐based
search
and
link
nagiva(on.

This
simplicity
has
been
one
of
the
great

strengths
of
the
web,
and
has
been
an

important
factor
in
its
popularity
and
their
own

content.

The
Seman(c
Web
&
Ontologies
9

Shortcomings

Examples:

•  Finding
informa(on
about
people
with
very

common
names
can
be
a
frustra(ng
experience.

•  Answering
more
complex
queries
along
with

more
general
informa(on
retrieval,
integra(on,

sharing
and
processing
can
be
diﬃcult
….
We

have
seen
that…

The
Seman(c
Web
&
Ontologies
10

Some
solu(ons

•  Sosware
glue:
Mashups

–  loca(on
informa(on
from
one
source
might
be
combined
with

map
informa(on
from
another
source
in
order
to
show
the

loca(on
of
and
provide
direc(ons
to
points
of
interest
such
as

hotels
and
restaurants.

•  Tagging
via
social
networks
(Web
2.0)

–  harness
the
power
of
user
communi(es
in
order
to
share
and

annotate
informa(on.

•  Examples
include
image
and
video
shar-‐ing
sites
such
as
Flickr
and

YouTube,
and
auc(on
sites
such
as
eBay.

–  In
these
applica(ons,
annota(ons
usually
take
the
form
simple
tags,
such
as

”each",
”birthday",
”family"
and
”friends".
The
meaning
of
tags
is,
however,

typically
not
well
deﬁned,
and
may
be
impenetrable
even
to
human
users:

typ-‐ical
examples
(from
Flickr)
include
"asquatchmusicfes(val",

"elebritylookalikes",
and
"wab08".

The
Seman(c
Web
&
Ontologies
11

The
”travel
agent”

•  The
classic
example
of
a
seman(c
web
applica(on
is
an

automated
travel
agent
that,
given
various
constraints

and
preferences,
would
oﬀer
the
user
suitable
travel
or

vaca(on
sugges(ons.

•  A
key
feature
of
such
a
"sosware
agent"
is
that
it

would
not
simply
exploit
a
predetermined
set
of

informa(on
sources,
but
would
search
the
web
for

relevant
informa(on
in
much
the
same
way
that
a

human
user
might
do
when
planning
a
vaca(on.

The
Seman(c
Web
&
Ontologies
12

The
goal

•  The
goal
of
the
Seman(c
Web
is
to
allow
web

informa(on
and
services
to
be
more

eﬀec(vely
exploited
by
humans
and

automated
tools.

The
Seman(c
Web
&
Ontologies
13

Seman(c
Web

•  The
focus
of
the
seman(c
web
is
to
share
data

instead
of
documents.

•  In
other
words,
it
is
a
project
that
should
provide

a
common
framework
that
allows
data
to
be

shared
and
reused
across
applica(on,
enterprise,

and
community
boundaries.

•  It
is
a
collabora(ve
eﬀort
led
by
World
Wide
Web

Consor(um
(W3C).

The
Seman(c
Web
&
Ontologies
14

Semantic Web & Ontologies
•  How
are
we
going
to
represent
meaning
and
knowledge
on
the
web?

•  A
key
idea
behind
the
seman&c
web
is
to
address
this
problem
by
giving

machine-‐accessible
seman&cs
via
annota&on.

•  Knowledge
is
represented
in
the
form
of
rich
conceptual
schemas
called

ontologies.

•  Ontologies
are
the
backbone
of
the
Seman(c
Web.

•  Ontologies
are
rich
conceptual
schemas
that
give
formally
deﬁned

meanings
to
the
terms
used
in
annota&ons,
transforming
them
into

seman&c
annota&ons.

•  They
provide
the
knowledge
that
is
required
for
seman(c
applica(ons
of
all

kinds.

15The
Seman(c
Web
&
Ontologies

Main
Diﬃculty

•  Current
web
content
is
intended
for
humans

(HTML
markup
with
layout,
images
and
other

presenta(onal
features).

•  Humans
understand
this
content,
but

machines
can’t.

The
Seman(c
Web
&
Ontologies
16

Basically...
•  Ontologies provide a shared understanding of a domain.
•  They provide background knowledge to automatize certain tasks.
•  By the process of annotation, knowledge can be linked to
ontologies.
–  Example: “Angelina Jolie” (Text) linked to concept Actress
–  In our ontology we also know that an actress always is female and a
person.
•  Ontologies allow the creation of annotations à machine-readable
and machine-understandable content.
•  If machines can understand content, they can also perform more
meaningful and intelligent queries.
–  Distinction of Jaguar the animal and the car.
–  Combination of information that is distributed on the Web.
17The
Seman(c
Web
&
Ontologies

Old
and
New
Issues

Old
ones:

•  knowledge
representa(on

•  Reasoning

•  Harnessing
the
idiosyncracies
of
natural
languages

•  …

New
ones:

•  integra(ng
diﬀerent
ontologies
may
prove
to
be
at
least
as

hard
as
integra(ng
the
resources
that
they
describe

•  Crea(on
of
suitable
annota(ons

•  …

The
Seman(c
Web
&
Ontologies
18

Regardless
these
issues…

•  …
considerable
progress
has
been
made
in
the

development
of
the
infrastructure
needed
to

support
the
seman(c
web.

•  In
par(cular,
there
has
been
impressive

progress
in
the
development
of
languages
and

tools
for
content
annota(on
and
for
the

design
and
deployment
of
ontologies.

The
Seman(c
Web
&
Ontologies
19

Seman(c
Annota(on

•  To
facilitate
the
process
of
seman(c

annota(on,
RDF
and
OWL
have
been

developed
as
standard
formats
fo
the
sharing

and
integra(on
of
data
and
knowledge.

•  RDF
and
OWL
are
standards:

– RDF
(Resource
Descrip(on
Framework)

– OWL
(Web
Ontology
Language)

The
Seman(c
Web
&
Ontologies
20

Ontologies

(Metaphysics)

•  Ontology,
in
its
original
philosophical

sense,
is
a
fundamental
branch
of

metaphysics
focusing
on
the
study
of

existence.

•  Its
objec(ve
is
to
determine
what

en((es
and
types
of
en((es
actually

exist,
and
thus
to
study
the
structure

of
the
world.

•  The
study
of
ontology
can
be
traced

back
to
the
work
of
Plato
and

Aristotle,
and
includes
the

development
of
hierarchical

categorisa(ons
of
diﬀerent
kinds
of

en((es
and
the
features
that

dis(nguish
them

The
Seman(c
Web
&
Ontologies
21

Tree
of
Porphyry

Tree
of
Porphyry,

III
AD

•  The
Porphyrian
tree,
Tree
of
Porphyry
or

Arbor
Porphyriana
is
a
classic
device
for

illustra(ng
what
is
also
called
a
"scale
of

being".
It
was
suggested
by
the
3rd
century

AD
Greek
neoplatonist
philosopher
and

logician
Porphyry

The
Seman(c
Web
&
Ontologies
22

Ontology
(Computer
Science,
AI,
LT,
IR…)

•  Engineering
artefact,
usually
a
model
of
some

aspect
of
the
world.

•  It
introduces
vocabulary
describing
various

aspects
of
the
domain
being
modelled,
and

provides
an
explicit
specifica(on
of
the
intended

meaning
of
the
vocabulary.

•  This
specifica(on
osen
includes
classifica(on-‐
based
informa(on,
not
unlike
that
in
Porphyry's

tree.

The
Seman(c
Web
&
Ontologies
23

What is an ontology (i)?
24
“An
ontology
is
a
formal,
explicit
speciﬁca&on
of
a

shared
conceptualiza&on”

Studer,
Benjamins,
Fensel.
Knowledge
Engineering:
Principles
and
Methods.
Data
and
Knowledge
Engineering.
25
(1998)
161-‐197

An ontology is an explicit specification of a conceptualization
Gruber, T. A translation Approach to portable ontology specifications. Knowledge Acquisition. Vol. 5. 1993. 199-220
Abstract model and
simplified view of some
phenomenon in the world
that we want to represent
Machine-readable
Concepts, properties
relations, functions,
constraints, axioms,
are explicitly defined
Consensual
Knowledge
The
Seman(c
Web
&
Ontologies

What is an ontology (ii)?
•  An ontology is a hierarchically structured set of terms for describing a
domain that can be used as a skeletal foundation for a knowledge
base
B. Swartout; R. Patil; k. Knight; T. Russ. Toward Distributed Use of Large-Scale Ontologies Ontological
Engineering. AAAI-97 Spring Symposium Series. 1997. 138-148
•  An ontology defines the basic terms and relations comprising
the vocabulary of a topic area, as well as the rules for
combining terms and relations to define extensions to the
vocabulary
Neches, R.; Fikes, R.; Finin, T.; Gruber, T.; Patil, R.; Senator, T.; Swartout, W.R. Enabling Technology
for Knowledge Sharing. AI Magazine. Winter 1991. 36-56
•  An ontology provides the means for describing explicitly the
conceptualization behind the knowledge represented in a knowledge
base
A. Bernaras;I. Laresgoiti; J. Correra. Building and Reusing Ontologies for Electrical Network Applications
ECAI96. 12th European conference on Artificial Intelligence. Ed. John Wiley & Sons, Ltd.
298-302
25The
Seman(c
Web
&
Ontologies

Examples

•  Top
level
ontology:
Standard
Upper
Ontology

–  In
informa(on
science,
an
upper
ontology
(also
known
as
a
top-‐
level
ontology
or
founda(on
ontology)
is
an
ontology
(in
the

sense
used
in
informa(on
science)
which
describes
very
general

concepts
that
are
the
same
across
all
knowledge
domains.

•  Linguis(c
ontology:
WordNet

•  General
Ontology:
Cyc,
UNSPSC,
ecl@ss

•  Domain
ontology:
MeSH
(Medical
Subject
Headings),

CHEMICALS,
UMLS

•  Research
ontology:
KA2
(Knowledge
Acquisi(on

Community
Ontology)

The
Seman(c
Web
&
Ontologies
26

Resource
Descrip(on
Framework
(i)

•  A
language
that
has
been
developed
in
order
to

provide
a
extensible
mechanism
for
describing
web

resources
and
rela(onships
between
them.

•  A
key
feature
of
RDF
is
the
use
of
Interna(onalized

Resource
Iden(ﬁers
(IRIs)
(which
is
a
generalisa(on
of

Uniform
Resource
Locators
(URLs)
to
refer
to

resources.

•  RDF
is
a
very
simple
language:
its
underlying
data

structure
is
a
labelled
directed
graph,
and
its
only

syntac(c
construct
is
the
triple.

•  A
triple
consists
of
three
components,
referred
to
as

the
subject,
predicate
and
object.

The
Seman(c
Web
&
Ontologies
27

a
directed
graph

is
a
set
of
nodes

connected
by

edges,
where

the
edges
have
a

direc(on

associated
with

them.

/ˈaɪˌɑːˌraɪ/

RDF
(ii)

•  More
formally,

a
triple
represents
a
single
edge
(labelled

with
the
predicate)
connec(ng
two
nodes
(labelled
with

the
subject
and
object);
it
describes
a
binary
rela(onship

between
the
subject
and
object
via
the
predicate.

•  The
predicate
of
a
triple
is
always
an
IRI,
and
an
IRI
that
is

used
in
the
predicate
posi(on
of
a
triple
is
called
a

property.

•  A
set
of
triples
is
called
an
RDF
graph.

•  In
order
to
facilitate
the
sharing
and
exchanging
of
graphs

on
the
web,
an
XML
serialisa(on
has
also
been
deﬁned.

The
Seman(c
Web
&
Ontologies
28

”Harry
PoNer
has
a
pet
called
Hedwig…”

The
Seman(c
Web
&
Ontologies
29

RDF/XML

RDF
graph

Lect
09:
Rela(on
Extrac(on:

DBPediaRela(on
database
that
draw
from
Wikipedia

•  Resource
Descrip&on
Framework
(RDF)
triples

subject
predicate
object

Golden Gate Park location San Francisco!
dbpedia:Golden_Gate_Park

dbpedia-‐owl:loca(on

dbpedia:San_Francisco

!
•  DBPedia:
The
DBpedia
project
uses
the
Resource

Descrip(on
Framework
(RDF)
to
represent
the
extracted

informa(on
and
consists
of
3
billion
RDF
triples,
580
million

extracted
from
the
English
edi(on
of
Wikipedia
and
2.46

billion
from
other
language
edi(ons
(wikipedia,
March

2016).

30
The
Seman(c
Web
&
Ontologies

…
but
…
not
enough…

•  Capabili(es
of
RDF
as
ontology
language
are

limited

– No
cardinality

– No
possible
to
describe
conjunc(on
of
classes

– …

RDF
is
a
very
simple
language

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Ontologies
31

cardinality
of
a
set
is
a
measure

of
the
"number
of
elements
of

the
set”.
For
example,
the
set
A

=
{2,
4,
6}
contains
3
elements,

and
therefore
A
has
a

cardinality
of
3

Need
for
a
more
expressive
ontology
language:

OWL
(Web
Ontology
Language)

•  Since
the
architecture
of
the
web
depends
on
agreed

standards,
the
World
Wide
Web
Consor(um
(W3C)
set

up
a
standardisa(on
working
group
to
develop
a

standard
for
a
web
ontology
language.

• 
The
result
of
this
ac(vity
was
the
OWL
ontology

language
standard.

•  The
integra(on
of
OWL
with
RDF
has
the
advantage
of

making
OWL
ontologies
directly
accessible
to
web

based
applica(ons.

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32

Back
Story:

hNp://ileriseviye.wordpress.com/2011/11/01/why-‐web-‐
ontology-‐language-‐is-‐abbreviated-‐as-‐owl-‐and-‐not-‐wol/

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Descrip(on
Logics
(DLs)

•  A
key
feature
of
OWL
is
its
basis
in
Descrip(on

Logics,
a
family
of
logic-‐based
knowledge

representa(on
formalisms
that
have
a
formal

seman(cs
based
on
ﬁrst-‐order
logic
(FOL).

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34

Descrip(on
Logics

•  We
can
use
DLs
to
model
an
applica(on
domain.
The
focus
is

then
on:

–  Representa(on
of
knowledge
about
categories

–  The
set
of
categories
in
an
applica(on
domain
is
called

terminology

–  The
terminology
is
arranged
in
a
hierachical
organiza&on
called

ontology,
which
capture
superset
&
subset
rela(ons
among

categoires/concepts.

–  In
order
to
specify
a
hierachical
structure,
we
can
use

subsump$on
rela(ons
betw
the
appropriate
concepts
in
a

terminiology

–  Subsump$on
is
a
form
of
inference.
Determines
whether
a

superset/subset
rela(on
(based
on
the
fact
asserted
in
a

terminology)
exists
betw
two
concepts.

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35

In
short,
DLs
are…

•  …
formalisms
based
on
an
object-‐oriented

modelling,
in
which
the
domain
is
described
in

terms
of
individuals
(instances),
concepts

(classes),
and
roles
(proper(es/predicates):

–  individuals,
e.g.,
"Hedwig",
are
the
basic
elements
of

the
domain;

–  concepts,
e.g.,
"Owl",
describe
sets
of
individuals

having
similar
characteris(cs;

–  roles,
e.g.,
"hasPet",
describe
rela(onships
between

pairs
of
individuals,
such
as
"HarryPoNer
hasPet

Hedwig".

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36

Axioms

•  An
OWL
ontology
consists
of
a
set
of
axioms

•  Exemple:

–  given
the
axiom
C
equivalentClass
D,
then
an
individual
is
an
instance
of
C
if
and

only
if
it
is
an
instance
of
D.

–  i.e.
Combining
axioms
with
class
descrip(ons
allows
for
easy
extension
of
the

vocabulary
by
introducing
new
names
as
abbrevia(ons
for
descrip(ons.

See
the
following
axiom:

Class: HogwartsStudent!
!EquivalentTo: Student and attendsSchoolvalue Hogwarts!

introduces
the
class
name
HogwartsStudent,
and
asserts
that
its
instances
are

just
those
Students
who
aNend
Hogwarts.

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37

TBox
&
ABox

•  Axioms
describe
constraints
on
the
structure
of
the

domain:

–  in
DLs
such
a
set
of
axioms
is
called
a
TBox
(Terminology

Box).

•  OWL
also
allows
for
axioms
asser&ng
facts
about
some

concrete
situa(on,
similar
to
data
in
a
database
se€ng:

–  in
DLs
such
a
set
of
axioms
is
called
an
ABox
(Asser(on
Box).

The
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38

Decid-‐ability
(i)

•  Descrip(on
Logics
are
fully-‐ﬂedged
logics
and

so
have
a
formal
seman(cs.

• 
DLs
can
be
seen
as
decidable
subsets
of
FOL

with:

– 
individuals
being
equivalent
to
constants,

– concepts
to
unary
predicates,

– roles
to
binary
predicates.

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39

FOL
…
undecidable
(some(mes)

•  The
Incompleteness
Theorem
,
proven
in

1930,
demonstrates
that
ﬁrst-‐order
logic
is
in

general
undecidable.

•  That
means
there
exist
statements
in
this
logic

form
that,
under
certain
condi(ons,
cannot
be

proven
either
true
or
false.

•  Ex:
can’t
solve
the
Hal$ng
Problem

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&
Ontologies
40

Hal(ng
Problem

•  In
1936
Alan
Turing
proved
that
it's
not
possible
to
decide
whether

an
arbitrary
program
will
eventually
halt,
or
run
forever.

•  The
official
defini&on
of
the
problem
is
to
write
a
program
(actually,

a
Turing
Machine*)
that
accepts
as
parameters
a
program
and
its

parameters.
That
program
needs
to
decide,
in
finite
&me,
whether

that
program
will
ever
halt
running
these
parameters.

•  The
hal(ng
problem
is
a
cornerstone
problem
in
computer
science.

It
is
used
mainly
as
a
way
to
prove
a
given
task
is
impossible,
by

showing
that
solving
that
task
will
allow
one
to
solve
the
hal(ng

problem.

*A
Turing
machine
is
a
hypothe(cal
device
that
manipulates
symbols

according
to
a
table
of
rules.
Despite
its
simplicity,
a
Turing
machine

can
be
adapted
to
simulate
the
logic
of
any
computer
algorithm,

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&
Ontologies
41

Decid-‐ability
(ii)

•  DLs
give
a
precise
and
unambiguous
meaning

to
descrip(ons
of
the
domain

•  This
also
allows
for
the
development
of

reasoning
algorithms
that
can
provide
correct

answers
to
arbitrarily
complex
queries
about

the
domain.

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Ontologies
42

Reasoning:

OWL
vs
Databases

OWL
axioms
behave
like
inference
rules
rather
than
database
constraints.

!
Class: Phoenix!
!SubClassOf: isPetOf only Wizard!
!
Individual: Fawkes!
Types: Phoenix!
Facts: isPetOf Dumbledore!
•  Fawkes
is
said
to
be
a
Phoenix
and
to
be
the
pet
of
Dumbledore,
and
it
is
also
stated
that
only
a

Wizard
can
have
a
pet
Phoenix.

•  In
OWL,
this
leads
to
the
implica(on
that
Dumbledore
is
a
Wizard.
That
is,
if
we
were
to
query
the

ontology
for
instances
of
Wizard,
then
Dumbledore
would
be
part
of
the
answer.

•  In
a
database
se€ng
the
schema
could
include
a
similar
statement
about
the
Phoenix
class,
but
in

this
case
it
would
be
interpreted
as
a
constraint
on
the
data:
adding
the
fact
that
Fawkes
isPetOf

Dumbledore
without
Dumbledore
being
already
known
to
be
a
Wizard
would
lead
to
an
invalid

database
state,
and
such
an
update
would
therefore
be
rejected
by
a
database
management

system
as
a
constraint
viola(on.

The
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Web
&
Ontologies
43

Ontology
Development
Tools

•  State
of
the
art
ontology
development
tools,
such

as
SWOOP,
Protégé,
and
TopBraid
Composer,
use

DL
reasoners
to
provide
feedback
to
the
user
about

the
logical
implica(ons
of
their
design:

– i.e.
warnings
about
inconsistencies
and
synonyms.

The
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&
Ontologies
44

WebProtégé

hNp://protege.stanford.edu/products.php#web-‐protege

The
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Ontologies
45

VOWL:

Visual
Nota(on
for
OWL

Ontologies

hNp://vowl.visualdataweb.org/v2/

The
Seman(c
Web
&
Ontologies
46

Domain-‐speciﬁc
ontologies

•  The
availability
of
tools
has
contributed
to
the

increasingly
widespread
use
of
OWL,
and
it
has

become
the
de
facto
standard
for
ontology

development
in
ﬁelds
as
diverse
as

–  Biology

–  Medicine

–  Geography

–  Geology

–  Agriculture

–  Defence

–  etc

The
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Ontologies
47

Complex
Queries

•  The
use
of
DL
reasoners
allows
OWL
ontology

applica(ons
to
answer
complex
queries
and
to

provide
guarantees
about
the
correctness
of
the

result.

•  Reliability
and
correctness
are
clearly
important

features
of
any
informa(on
system;

•  They
are
par(cularly
important
if
ontology
based

systems
are
to
be
used
in
safety-‐cri(cal

applica(ons
such
as
medicine,
where
incorrect

reasoning
could
adversely
impact
pa(ent
care.

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48

Standard
Query
Language

•  It
has
long
been
recognised
that
the
seman(c

web,
and
seman(c
web
knowledge

representa(on
languages
such
as
RDF
and

OWL,
would
also
beneﬁt

from
the
availability

of
a
standardised
query
language
such
as
SQL

•  A
W3C
standardisa(on
working
group
was
set

up,
and
has
completed
its
work
on
the

SPARQL
query
language
standard.

The
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49

SPARQL
Protocol
and
RDF
Query
Language

…

•  …
is
an
RDF
query
language,
ie
a
query

language
that
can
retrieve
and
manipulate

data
stored
in
RDF
format
(ie
triples).

•  SPARQL
allows
for
a
query
to
consist
of
triple

paSerns,
conjunc(ons,
disjunc(ons,
and

op(onal
paNerns

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&
Ontologies
50

Tags
&
Ontologies

•  Tagging
facili(es
within
Web
2.0
applica(ons

have
shown
how
it
might
be
possible
for
user

communi(es
to
collabora(vely
annotate
web

content,
and
create
simple
forms
of
ontology

via
the
development
of
hierarchically

organised
sets
of
tags,
osen
called

folksonomies….

The
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51

Challenges

•  Currently
hard
to
combine:

– Increased
expressive
power
(by
using
more

sophis(cated
logics)
with
scalability
(large
ontologies)

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52

Ontology
Learning

•  Ontology
learning
(ontology
extrac(on,
ontology
genera(on,
or
ontology

acquisi(on)
is
the
automa(c
or
semi-‐automa(c
crea(on
of
ontologies,

including
extrac(ng
the
corresponding
domain's
terms
and
the

rela&onships
between
those
concepts
from
a
corpus
of
natural
language

text,
and
encoding
them
with
an
ontology
language
for
easy
retrieval.

•  As
building
ontologies
manually
is
extremely
labor-‐intensive
and
(me

consuming,
there
is
great
mo(va(on
to
automate
the
process.

•  Typically,
the
process
starts
by
extrac(ng
terms
and
concepts
or
noun

phrases
from
plain
text
using
linguis(c
processors
such
as
part-‐of-‐speech

tagging
and
phrase
chunking.
Then
sta(s(cal
techniques
are
used
to

extract
rela(on,
osen
based
on
Machine
Learning.

–  hNp://en.wikipedia.org/wiki/Ontology_learning

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53

In
summary…

Why
to
build
an
ontology?

•
To
share
common
understanding
of
the

structure
of
informa(on
among
people
or

sosware
agents

•
To
enable
reuse
of
domain
knowledge

•
To
make
domain
assump(ons
explicit

•
To
analyze
domain
knowledge

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54

How
to
build
an
ontology

Generally
speaking
(and
roughtly
said),
when

designing
an
ontology,
four
main
components

are
used:

1.  Classes

2.  Rela(ons

3.  Axioms

4.  Instances

The
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&
Ontologies
55

Classes

•
concepts
of
the
domain
or
tasks,
which
are

usually
organized
in
taxonomies

Ex:
in
a
university
ontology,
student
and

professor
are
two
classes

The
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Web
&
Ontologies
56

Rela(ons

A
type
of
interac(on
between
concepts
of
the

domain:

Ex:
subclass-‐of
or
is-‐a

are
rela(ons

The
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57

Axioms

Asser(ons
that
are
always
true
for
the
domain

of
interest

Ex:
if
a
student
aNends
both
”Math”
and
”Basic

text
processing”
courses,

then
he
or
she
must

be
a
1st
year
student.

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58

Instances

Represent
speciﬁc
elements

Ex:
a
Student
called
Peter
is
the
instance
of

Student
class

The
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&
Ontologies
59

Important!

• 
There
is
no
single
correct
class
hierarchy
for

any
given
domain.

•  The
hierarchy
depends
on
the
possible
uses
of

the
ontology.

•  The
level
of
detail
is
depend
on
the
applica(ons

and
purposes.

The
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&
Ontologies
60

The
end

61
The
Seman(c
Web
&
Ontologies

Lecture: Ontologies and the Semantic Web

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Lecture: Ontologies and the Semantic Web