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Knowledge Representation, Modelling
Knowledge Representation, Modelling
and Processing in Modern Semantic Systems
and Processing in Modern Semantic Systems
Weronika T. Adrian
Weronika
T. Adrian
AGH University of Science
and Technology,
Department of Automatics
AGH University of Science and Technology, Department of Automatics
Abstract: Modern intelligent applications based on semantic technologies constitute
an interesting
class ofbased
Knowledge-Based
SysAbstract:
Modern intelligent
applications
on semantic tech-
tems.
Theconstitute
paper gives
overviewclass
of theof
methods
and tools used
in
nologies
an an
interesting
Knowledge-Based
Syssemantic
and an
presents
theof
research
concerning
adaptation
tems. Thesystems
paper gives
overview
the methods
and tools
used in
of well-founded
solutions
for Rule-Based
Systems
to theadaptation
semantic
semantic
systems
and presents
the research
concerning
systems
conditions.
Theoretical
solutions as
well as to
practical
impleof
well-founded
solutions
for Rule-Based
Systems
the semantic
mentations
are briefly
discussed.
Outlineasofwell
future
directions
and
systems
conditions.
Theoretical
solutions
as practical
impleresearch problems
is given.
mentations
are briefly
discussed. Outline of future directions and
research problems is given.
Keywords: semantic technologies, knowledge modelling, represen-
tation and reasoning,
rule-based systems,
ontology-driven
systems,
Keywords:
semantic technologies,
knowledge
modelling, represenintelligent
tation
and systems
reasoning, rule-based systems, ontology-driven systems,
intelligent systems
S
S
emantic technologies have gained a steadily increasing
popularity
in the field
intelligent
systems
over
emantic
technologies
haveofgained
a steadily
increasing
the last
decade. in
Knowledge
reasoning
popularity
the fieldrepresentation
of intelligent and
systems
over
based
explicitKnowledge
metadata representation
and ontologiesand
expressed
in
the
lastondecade.
reasoning
a
standarized
waymetadata
opens upand
possibilities
processing
based
on explicit
ontologiesofexpressed
in
knowledge
in distributed
environment.
Modern
a
standarized
way opens
up possibilities
of Knowledgeprocessing
Based Systems
(KBS) explore
”new Modern
ways and
methods
knowledge
in distributed
environment.
Knowledgeof
acquiring,
organizing
and spreading
knowledge”
in the
Based
Systems
(KBS) explore
”new ways
and methods
environment
increasingand
capacity
[1]. While
exploring
of
acquiring, of
organizing
spreading
knowledge”
in new
the
possibilities of
Semantic
Web and
semantic
systems,
it
environment
of the
increasing
capacity
[1]. While
exploring
new
is worthwhileoftothe
consider
howWeb
well established
andsystems,
grounded
possibilities
Semantic
and semantic
it
solutions
can to
beconsider
adaptedhow
or integrated.
is worthwhile
well established and grounded
solutions can be adapted or integrated.
1. Semantic Technologies
1. Semantic
Technologies
Semantic
technologies
[2] work on different levels of abstrac-
tion. At the
lowest level,
and serialization
Semantic
technologies
[2] existing
work onencoding
different levels
of abstracmethods
are
used,
to
provide
unified
representation
of retion. At the lowest level, existing encoding and serialization
sources.
Using
the
unified
representation,
basic
semantic
methods are used, to provide unified representation of relayer is introduced.
Main method
of this layer
is semantic
ascribing
sources.
Using the unified
representation,
basic
semantic
metadata
that
describe
relations
among
resources
layer is introduced. Main method of this layer is ascribing
(documents,
people,
object,
places
etc.).
Such
a
representasemantic metadata that describe relations among resources
tion, which forms
sort of places
a semantic
universal
(documents,
people,a object,
etc.). graph,
Such a isrepresentaand
flexible.
To
organize
the
metadata
into
structures
and
tion, which forms a sort of a semantic graph, is universal
express
more
information
(classification
of
objects,
relations
and flexible. To organize the metadata into structures and
betweenmore
whole
classes of (classification
objects etc.), ontologies
differexpress
information
of objects, of
relations
ent
kinds
are
used.
These
divide
into
upper-level
ontologies,
between whole classes of objects etc.), ontologies of differdomain
ontologies,
and into
mostupper-level
specific – application
ent kindsand
aretask
used.
These divide
ontologies,
ontologies.
Even
more
expressive
knowledge
domain and task ontologies, and most specificrepresentation
– application
can
be achieved
use of rules.
They are
able to exontologies.
Even with
morethe
expressive
knowledge
representation
press
complex
relations
among
classes
of
objects,
as well
as
can be achieved with the use of rules. They are able
to exintroduce
some
sort
of
dynamics
to
the
Knowledge-Based
press complex relations among classes of objects, as well as
System.
with
sort of rules is
introduceIntegrating
some sort ontologies
of dynamics
to selected
the Knowledge-Based
an
active
research
area
in
the
Semantic
Web
community.
System. Integrating ontologies with selected sort
of rules is
an active research area in the Semantic Web community.
2. Modern Semantic Systems – Examples
2. Modern
Semantic
There
are several
examples ofSystems
application –ofExamples
the Semantic
Web concepts
andexamples
technologies.
An interesting
There
are several
of application
of the direction
Semantic
is theconcepts
development
of ontology-driven
systems, where
an
Web
and technologies.
An interesting
direction
ontology
is the core of
of ontology-driven
the system, determining
knowlis the development
systems, its
where
an
edge and is
performance.
systemsdetermining
can be used its
in various
ontology
the core of Such
the system,
knowldomains.
An example is
a system
citizen
safety
in uredge
and performance.
Such
systemsfor
can
be used
in various
ban environment
developed
within
EU FP7
domains.
An example
is a system
forthe
citizen
safetyproject
in urINDECT
(http://indect-project.eu).
TheFP7
system
deban
environment
developed within the EU
project
scribed
in (http://indect-project.eu).
[3] is a knowledge-based application
combining
INDECT
The system
dedatabase
with ontological
representation
and
scribed intechnologies
[3] is a knowledge-based
application
combining
reasoning.technologies
While fundamental
(declarative)
knowledge
is
database
with ontological
representation
and
stored
in
an
ontology
that
can
be
reused
across
application
reasoning. While fundamental (declarative) knowledge is
boundaries,
operational
describing
changing
traffic
stored
in an ontology
thatfacts
can be
reused across
application
conditions
are
stored
in
a
database.
boundaries, operational facts describing changing traffic
Anotherare
important
conditions
stored in application
a database. of the semantic technologies
are important
Semantic Wikis
(see http://semanticweb.
Another
application
of the semantic techorg/wiki/Semantic_wiki).
the accessible
nologies are Semantic WikisThey
(see combine
http://semanticweb.
and user-friendly environmentThey
characteristic
wiki
org/wiki/Semantic_wiki).
combine of
theregular
accessible
systems
with improved
possibilities
of knowledge
represenand user-friendly
environment
characteristic
of regular
wiki
tation. Knowledge
representation
reasoning represenmethods
systems
with improved
possibilitiesand
of knowledge
vary
between
different
implementations
of the semantic
tation.
Knowledge
representation
and reasoning
methods
wikis.between
Basic semantic
annotations
allow users
to assign
catvary
different
implementations
of the
semantic
egories
to wiki
pages,annotations
define attributes
of the
described
wikis. Basic
semantic
allow users
to assign
catobjects
relations
between
Navigation
queryegories and
to wiki
pages,
define them.
attributes
of theand
described
ing enriched
in this way
allowthem.
semantic
wikis to
as
objects
and relations
between
Navigation
andserve
querysemantic
encyclopedias
in
various
domains.
A
proposal
of
ing enriched in this way allow semantic wikis to serve as
a
semantic
wiki
for
dieticians
has
been
formulated
in
[4].
semantic encyclopedias in various domains. A proposal of
More advanced
methods
beformulated
introduced in
to [4].
this
a semantic
wiki forKR
dieticians
hasmay
been
classMore
of systems.
A KR
semantic
knowledge-based
wiki Loki
[5]
advanced
methods
may be introduced
to this
provides
diverseAknowledge
acquisition methods,
while
class
of systems.
semantic knowledge-based
wiki Loki
[5]
using a unified
representation
based on while
logic.
provides
diverseunderlying
knowledge
acquisition methods,
This
to integrate
therepresentation
system with other
and
usingenable
a unified
underlying
basedtools
on logic.
solutions.
Loki
a platform
knowledge
engineering
[6]
This
enable
to is
integrate
the for
system
with other
tools and
and a potential
for for
knowledge
verification
[7]. [6]
solutions.
Loki isplatform
a platform
knowledge
engineering
and a potential platform for knowledge verification [7].
3. Integrating Rule-Based Solutions
3. with
Integrating
Rule-Based
Solutions
Semantic
Technologies
3.1.with
Rule-Based
Systems
and Technologies
Semantic
Technologies
3.1.
Rule-Based
and
Technologies
Reasoning
beyond Systems
classification
tasks
and queries is possible
thanks
to
knowledge
representation
onisrules.
Reasoning beyond classification tasks andbased
queries
posWell
established
solutions
for
Rule-Based
Systems
sible thanks to knowledge representation based on(RBS)
rules.
include
formalization
of the
[8] as well
as
Well
established
solutions
forrepresentation
Rule-Based Systems
(RBS)
the
whole
process
of
knowledge
engineering
[9],
advanced
include formalization of the representation [8] as well as
algorithms
for modularized
rule bases
[10] and
the whole process
of knowledge
engineering
[9],verification
advanced
of
RBS
[11].
It
is
worthwhile
to
investigate
the sealgorithms for modularized rule bases [10] andhow
verification
mantic
systems
could
benefit
from
the
achievements
of the
of RBS [11]. It is worthwhile to investigate how the
semantic systems could benefit from the achievements of the
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RBS
methodologies. The
complete design
and verification
RBS
RBS methodologies.
methodologies. The
The complete
complete design
design and
and verification
verification
framework
for
hybrid
intelligent
systems
HaDEs
[12] has
framework
for
hybrid
intelligent
systems
HaDEs
framework for hybrid intelligent systems HaDEs [12]
[12] has
has
been
used
to
implement
prototype
solutions.
been
used
to
implement
prototype
solutions.
been used to implement prototype solutions.
3.2.
Research Directions
and Results
3.2.
3.2. Research
Research Directions
Directions and
and Results
Results
Research
on integration
of the
RBS solutions
and semantic
Research
Research on
on integration
integration of
of the
the RBS
RBS solutions
solutions and
and semantic
semantic
technologies
have
been
conducted
on
several
levels.
First of
technologies
have
been
conducted
on
several
levels.
technologies have been conducted on several levels. First
First of
of
all,
possibility
of
integration
of
the
logical
foundations
of
all,
possibility
of
integration
of
the
logical
foundations
all, possibility of integration of the logical foundations of
of
a
class of
RBS and
ontologies have
been investigated.
The
a
a class
class of
of RBS
RBS and
and ontologies
ontologies have
have been
been investigated.
investigated. The
The
most
widely
used
formalism
for
ontologies
are
Description
most
most widely
widely used
used formalism
formalism for
for ontologies
ontologies are
are Description
Description
Logics
(DL) [13].
For a
formalized description
of RBS,
Logics
Logics (DL)
(DL) [13].
[13]. For
For a
a formalized
formalized description
description of
of RBS,
RBS,
the
ALSV(FD)
Attribute
Logic
[14]
has
been
used.
An
the
ALSV(FD)
Attribute
Logic
[14]
has
been
used.
the ALSV(FD) Attribute Logic [14] has been used. An
An
integration
proposal
for
a
hybrid
formalism,
supported
by
integration
integration proposal
proposal for
for a
a hybrid
hybrid formalism,
formalism, supported
supported by
by
a
dedicated
language
DAAL
(Description
and
Attribute
a
a dedicated
dedicated language
language DAAL
DAAL (Description
(Description and
and Attribute
Attribute
Logics),
has been
described in
[15].
Logics),
Logics), has
has been
been described
described in
in [15].
[15].
Based
on
this
proposal,
a
prototype
implementation
Based
Based on
on this
this proposal,
proposal, a
a prototype
prototype implementation
implementation
of
a
system
combining
ontology
and
rule
reasoning called
of
of a
a system
system combining
combining ontology
ontology and
and rule
rule reasoning
reasoning called
called
Pellet-HeaRT
has
been
proposed
in
[16].
Pellet
is a
widely
Pellet-HeaRT
has
been
proposed
in
[16].
Pellet
Pellet-HeaRT has been proposed in [16]. Pellet is
is a
a widely
widely
used
tool for
reasoning in
DL-based ontologies,
while
used
used tool
tool for
for reasoning
reasoning in
in DL-based
DL-based ontologies,
ontologies, while
while
HeaRT
[17]
is
a
dedicated
rule
engine
able
to
operate
HeaRT
HeaRT [17]
[17] is
is a
a dedicated
dedicated rule
rule engine
engine able
able to
to operate
operate
in
various inference
modes on
modularized rule
bases.
in
in various
various inference
inference modes
modes on
on modularized
modularized rule
rule bases.
bases.
Another
research
direction
is
enhancing
semantic
Another
Another research
research direction
direction is
is enhancing
enhancing semantic
semantic
knowledge-based
wikis
with
rule
reasoning.
To
this
end, the
knowledge-based
knowledge-based wikis
wikis with
with rule
rule reasoning.
reasoning. To
To this
this end,
end, the
the
HeaRT
engine
has
been
embedded
in
the
Loki
system
[18].
HeaRT
engine
has
been
embedded
in
the
Loki
system
HeaRT engine has been embedded in the Loki system [18].
[18].
The
resulted system
combines the
flexibility of
the semantic
The
The resulted
resulted system
system combines
combines the
the flexibility
flexibility of
of the
the semantic
semantic
representation
with
strong
rule-based
knowledge
represenrepresentation
with
strong
rule-based
knowledge
representation with strong rule-based knowledge represenrepresentation
and reasoning.
Knowledge engineering
possibilities
tation
tation and
and reasoning.
reasoning. Knowledge
Knowledge engineering
engineering possibilities
possibilities
in
such
a
hybrid
system
has
been
recently
described
in [19].
in
in such
such a
a hybrid
hybrid system
system has
has been
been recently
recently described
described in
in [19].
[19].
4.
Summary and
Outlook
4.
4. Summary
Summary and
and Outlook
Outlook
New
context of
the systems
equipped with
semantic techNew
New context
context of
of the
the systems
systems equipped
equipped with
with semantic
semantic techtechnologies
has
been
explored
in
order
to
investigate
posnologies
has
been
explored
in
order
to
investigate
nologies has been explored in order to investigate pospossibilities
of
adapting
solutions
originally
developed
for
sibilities
sibilities of
of adapting
adapting solutions
solutions originally
originally developed
developed for
for
rule-based
systems.
Integration
proposal
of
Attribute
Logic
rule-based
rule-based systems.
systems. Integration
Integration proposal
proposal of
of Attribute
Attribute Logic
Logic
and
Description Logics
has been
formulated. A
prototype
and
and Description
Description Logics
Logics has
has been
been formulated.
formulated. A
A prototype
prototype
implementation
of
a
hybrid
system
combining
ontologyimplementation
implementation of
of a
a hybrid
hybrid system
system combining
combining ontologyontologybased
representation
with
rule-based
reasoning
based representation
representation with
with rule-based
rule-based reasoning
reasoning has
has been
been
based
has
been
developed.
A
semantic
wiki
with
unified
rule-based
developed. A
A semantic
semantic wiki
wiki with
with unified
unified rule-based
rule-based reprepdeveloped.
representation
resentation has
has been
been proposed
proposed and
and tested
tested in
in the
the field
field of
of
resentation
has
been
proposed
and
tested
in
the
field
of
knowledge
knowledge engineering.
engineering.
knowledge
engineering.
For
For future
future work,
work, issues
issues and
and challenges
challenges of
of Business
Business ProProFor
future
work,
issues
and
challenges
of
Business
Processes
and
Business
Rules
[20]
are
planned
cesses and
and Business
Business Rules
Rules [20]
[20] are
are planned
planned to
to be
be investiinvesticesses
to
be
investigated.
gated. Support
Support for
for Business
Business Process
Process Model
Model and
and Notation
Notation
gated.
Support
for
Business
Process
Model
and
Notation
(BPMN)
in
wiki
followed
by
a
possibility
(BPMN) in
in wiki
wiki followed
followed by
by a
a possibility
possibility of
of collaboracollabora(BPMN)
of
collaborative
tive development
development of
of business
business processess
processess and
and rules
rules will
will be
be
tive
development
of
business
processess
and
rules
will
be
analyzed.
analyzed.
analyzed.
4.
4.
4.
5.
5.
5.
6.
6.
6.
7.
7.
7.
8.
8.
8.
9.
9.
9.
10.
10.
10.
11.
11.
11.
12.
12.
12.
Acknowledgment
Acknowledgment
Acknowledgment
The
paper is
supported by
the AGH
UST Grant
The
The paper
paper is
is supported
supported by
by the
the AGH
AGH UST
UST Grant
Grant
15.11.120.088.
15.11.120.088.
15.11.120.088.
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nauka
NAUKA
Reprezentacja, modelowanie i przetwarzanie
wiedzy w nowoczesnych systemach
Reprezentacja,semantycznych
modelowanie i przetwarzanie
wiedzy w nowoczesnych systemach
Streszczenie: Nowoczesne systemy inteligentne, wykorzystujące
semantycznych
technologie i języki semantyczne, stanowią interesują klasę sysStreszczenie:
systemy
inteligentne,
temów opartych Nowoczesne
na wiedzy. W tym
nowym
kontekściewykorzystujące
warto rozważyć
technologie
i języki
semantyczne,
stanowią
interesują klasę
syswykorzystanie
rozwiązań
wypracowanych
w klasycznych
sztucznej
temów
opartych
na wiedzy. W tym
nowym kontekście
warto
inteligencji,
w szczególności
systemach
regułowych.
Wrozważyć
artykule
wykorzystanie
rozwiązań
wypracowanych
w
klasycznych
sztucznej
nakreślono obszar badawczy obejmuący integrację istniejących
inteligencji,
w szczególności
systemach
regułowych.
artykule
metod i narzędzi
z tego obszaru
z rozwiązaniami
SieciWSemantynakreślono
obszar
badawczy
obejmuący
integrację
istniejących
cznej. Poruszono zarówno problem integracji wybranych formalmetod
narzędzi z tego
obszaru
z rozwiązaniami
Sieci
Semantyizmów istanowiących
podstawy
logiczne
reprezentacji
wiedzy
w obycznej.
Poruszono
zarówno
problem
integracji
wybranych
formaldwu dziedzinach, jak i praktyczne implementacje systemów.
izmów stanowiących podstawy logiczne reprezentacji wiedzy w obySłowa
kluczowe:
technologie
semantyczne,
modelowanie
dwu
dziedzinach,
jak i praktyczne
implementacje
systemów.
i reprezentacja wiedzy, wnioskowanie, systemy regułowe, systemy
Słowa
kluczowe:
technologie semantyczne, modelowanie
ontologiczne,
systemy inteligentne
i reprezentacja wiedzy, wnioskowanie, systemy regułowe, systemy
ontologiczne, systemy inteligentne
Weronika T. Adrian, MSc
Weronika T. Adrian (de domo Furmańska)
Weronika T. Adrian, MSc
is a research assistant in the Computer
Weronika
T. Adrian (de
domo Furmańska)
Science Laboratory,
Department
of Autois
a
research
assistant
in the Computer
matics, AGH UST. She graduated
in 2009
Science
Laboratory,
Department
of Autowith a degree
in Applied
Computer Science.
matics,
AGH
UST. She
graduated
in 2009
Her main
research
interests
cover Semanwith
a degree
in Appliedand
Computer
Science.
tic Web
technologies
logic programHer
main
research
cover
Seman-a
ming.
In her
masterinterests
thesis, she
proposed
tic
Web for
technologies
and
logic programmethod
using design
methods
for ruleming.
In
her
master
thesis,
she
a
based systems in the context of proposed
the Semanmethod
for
using
design
methods
for
ruletic Web. She has been involved in several national and european
based
systems
in the
contextBIMLOQ
of the Semanprojects,
including
HeKatE,
and INDECT.
tic
[email protected]
She has been involved in several national and european
e-mail:
projects, including HeKatE, BIMLOQ and INDECT.
e-mail: [email protected]
4/2010 Pomiary Automatyka Robotyka
3
4/2010 Pomiary Automatyka Robotyka 3
12/2011 Pomiary automatyka Robotyka 225