Ontology and Agent based Approach for Knowledge Management

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6 Νοε 2013 (πριν από 3 χρόνια και 8 μήνες)

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Ontology and Agent based
Approach for Knowledge
Management

Defense of PhD Thesis

Michal Laclav
í
k

Supervisor: Ing. Ladislav Hluch
ý

PhD.

Bratislava, 12th January 2006

2

Outline


Motivation


State of the Art


Objectives


Methodology and Tools


Agent Knowledge Model
–

Models,
Methodology, Library


Experience Management


Applications


Conclusion

Bratislava, 12th January 2006

3

Motivation and State of the Art


MAS is

powerful paradigm for distributed or heterogeneous
systems


MAS need Knowledge Support and Semantics


MAS need Connection with Existing Commercial Standards



Agent Technology Roadmap
: Current MAS Systems
–

lack of
Internal Agent Knowledge Model, lack of interconnection with
semantic web results (knowledge model representations) and
commercial standards



Focus on Agents and Knowledge representation (Ontologies)


Knowledge Management and Experience Management as
application domains

Bratislava, 12th January 2006

4

State of the Art
-

Agents


Agent Definition:

An agent is a computer
system capable of flexible autonomous action in
a dynamic, unpredictable and open environment.

(LUCK 2003)


MAS Standards:

FIPA, MASIF


Related to agent communication, agent platforms


No standards for internal agent knowledge model with
available implementations

Bratislava, 12th January 2006

5

State of the Art
-

Agents


Architectures:


Reactive Architecture


No specification of knowledge model, behavior of agent is based on
implemented responses to environment states


Belief Desire Intention Architecture
–

BDI


Belief
–

represents knowledge model, available some implementations
based on logic programming, not used in FIPA compliant MAS


Behavioral Architecture


FIPA compliant MAS are based on such architecture


No specification of Internal Agent Knowledge model
–

depend on agent
designer and developer


JADE Agent System


Support for ontologies based on FIPA
-
SL (Similar to First Order
Logic)


No Query engine


No Storage


No Inference


Bratislava, 12th January 2006

6

State of the Art
–

Ontologies, Knowledge



Ontologies


Knowledge Representation


OWL
-
DL compatible with Description
Logic


Query and Storage Engines available


RDF, OWL, RDQL based



Application domain


Knowledge Management

(KM) is the
process through which organizations
generate value from their intellectual and
knowledge
-
based assets

(Source: CIO Magazine)


Experience Management
is special
kind of KM
–

based on “lessons learned”


Characters

Data

Information

Knowledge

Actions

Syntax

Semantics

Pragmatics

Reasoning

(Bergman, 2002,

Experience Management)

Bratislava, 12th January 2006

7

Problem Specification

Multi Agent System

Agent 1

Agent 2

Agent 3

Graphical User Interface

External

System

Knowledge

Base

Directory

Facilitator

Knowledge Storage

Querying

XML, XML
-
RPC, SOAP

User requests

Displaying results

FIPA ACL,

KIF, FIPA
-
SL, FIPA
-
RDF

FIPA ACL,

RDF/OWL, RDQL

Knowledge

Model

KM

KM

IIOP,

HTTP,

SMTP

ACL

Bratislava, 12th January 2006

8

State of The Art Conclusion


Focus on
software
, intelligent and
FIPA

compliant
agents


Providing better
semantic infrastructure

(ontologies,
knowledge models
)


Apply basic principles of
software

and
knowledge engineering


Make stronger
connection

between MAS
and existing
commercial technologies

Bratislava, 12th January 2006

9

Thesis Objectives


Design of
Agent Architecture

using

Ontology
based

Knowledge Model


Design of
Software Development Methodology

for creation
of Agents with Ontology based Knowledge Model


Design of
Generic Ontology Model

for
Experience
Management

with extension to different application domains.


Design & Development of
Software Library

for building
Intelligent Agents with Ontology Knowledge Model with
possibility to plug agents to existing commercial technologies


Design and Development of user friendly
Knowledge
Presentation
.


Evaluation of Results

on real pilot operation.

Bratislava, 12th January 2006

10

Used Methods and Methodologies


Knowledge management, system design


Unified Modeling Language
–

UML


CommonKADS, MAScommonKADS


Protégé as Tool for CommonKADS


Formal methods for describing

ontology based models


Description Logic


Graph Ontology representation

Bratislava, 12th January 2006

11

Used Tools and Software


Protégé

Ontology Editor


Support for OWL ontology format


Can be used as modeling tool


JADE

(Java Agent DEvelopment
Framework)


Most developing MAS framework


Compliant with FIPA standards


Jena

–

Semantic Web Framework for Java


Support for OWL
–

best available OWL API


Support for RDQL model querying

Agent Knowledge Model

Objective:

Design of
Agent Architecture

using

Ontology
based

Knowledge Model


Bratislava, 12th January 2006

13

Agent Knowledge Model


Based on Events, Resources, Actions, Actors, Context


Formally Described using Sets, Description Logic
(compatible with OWL
-
DL), Graph Representation


Actor Context updating function/algorithm

(Actor Environment State)


C
A
new

= f
C
(e
a
,C
A
old
)


Resources updating function/algorithm

(result of fulfilled actor goals)


R
A
new

= f
R
(C
A
new
,R
A
old
)

Software Development
Methodology

Objective:

Design of
Software Development
Methodology

for creation of Agents
with Ontology based Knowledge Model



Bratislava, 12th January 2006

15

Development Methodology
(Knowledge Model)


Extending Model with Protégé Editor following
CommonKADS models


Organizational or Environment Model


Task Model


Agent or Actor Model


Includes implementation of algorithms for context and resource
updating


Results


Ontology developed in Protégé which can be exported in
OWL format.


Concrete Algorithms for each actor (often algorithms are
similar or same) which updates actors' context
C
A
new

and
resources
R
A
new
.

Bratislava, 12th January 2006

16

Development Methodology

(System Design)


UML Diagrams for concrete Application Domain


Use Case Diagram


for each agent


agent is taken as system

boundaries


Sequence Diagram


Communication among

agents


Class Diagram


Behaviors are described as methods


Agent Software Library

Objectives:


Design of
Agent Architecture

using

Ontology
based

Knowledge Model


Design & Development of
Software Library

for building
Intelligent Agents with Ontology Knowledge Model with
possibility to plug agents to existing commercial technologies


Bratislava, 12th January 2006

18

Agent Software Library


Support for OWL based Agent Knowledge Model


Support for XML
-
RPC connection to receive event and
send plain XML


Support for agent communication using FIPA ACL with
OWL and RDQL as content languages


Support for Presentation of Ontological Knowledge

(RDF/OWL => plain XML + XSL => HTML)


JADE and Jena Integration


Available on JADE official website

to MAS community

Bratislava, 12th January 2006

19

Agent Library Example

Support for Knowledge and
Experience Management

Objective:

Design of
Generic Ontology Model

for
Experience Management

with extension to
different application domains.

Bratislava, 12th January 2006

21

Extension of Model for
Experience Management


Extended Agent Memory
Model



Workflow Related


WfInstance, WfActivity


ActiveHint


Sub class of resource


Representation of
Experience


Employee

Bratislava, 12th January 2006

22

Algorithms for

EM Extension


Actor (Employee)
Context updating
algorithm


C
A
new

= f
C
(e
a
,C
A
old
)


Resources (Active Hint)
updating algorithm


R
A
new

= f
R
(C
A
new
,R
A
old
)



Bratislava, 12th January 2006

23

Complexity

of algorithms


All depends also on Active Hints
Templates count
–

this does not grow
too fast.


1
st

Case: Constant
–

final count of
context elements (1
-
6)


2
nd

Case: O(n)
–

based on
resource/event count in Memory


3
rd

Case: O(n
2
)
–

based on 2 loops:
events/resources, similar resources


experimental solution because algorithm
used other software e.g. Jena with RDQL
–

it was hard to prove complexity
different way.


Bratislava, 12th January 2006

24

Resource Similarity (3
rd

Case)


Similarity of Ontology
Individuals


Weighted matching of
properties


Similar to CBR
algorithm Weighted
Euclidian Distance


sim({res
1
,res
2
}) = fsim
(



"
{prop
i
}

property
i
.Resource({res
1
})




"
{prop
j
}

property
j
.Resource({res
2
})



{prop
i
}


{prop
j
}

{prop
i
}

DomainClass



DomainClass

Domain



$
{simWeight}

SimilarityWeight



domainClass.SimilarityWeight( DomainClass)

{simWeight}



{weight}

weight .SimilarityWeight( DomainClass)

{simWeight};


S
ij
{weight}/n

)

Presentation of Ontology
based Knowledge

Objective:

Design and Development of user
friendly
Knowledge Presentation
.

Bratislava, 12th January 2006

26

Presentation of Ontology
based Knowledge


Ontology Tree


Browse window


Graph


XSL Transformation


RDF/OWL => Plain XML +
XSL => HTML


Infrastructure to receive
plain XML using XML
-
RPC

Applications

Objective:

Evaluation of Results

on real
pilot operation.

Bratislava, 12th January 2006

28

Pellucid 5FP IST Project


Title:
Platform for Organizationally
Mobile Public Employees


Duration:

Sep 2002
-

Dec 2004


Knowledge Management to support
employees


Workflow based Administration
Processes


To support Employee Mobility in
organization


Agent Architecture based on
autonomous co
-
operating agents

Process Layer

Interaction Layer

Pellucid Architecture

Pellucid Agents

Bratislava, 12th January 2006

29

Pellucid Applications


CDG
, Genoa, Italy

Traffic Light Management


MMBG
, Sanlucar, Spain

Project Management


SADESI
, Seville, Spain

Telephone Incidence
Resolution

Bratislava, 12th January 2006

30

K
-
Wf Grid 6FP IST Project


Work on new

EMBET

architecture


Current state:

User Assistant Agent in K
-
Wf Grid uses model
presented in thesis.


Algorithms presented in chapter 5 were reused with same
improvements and modifications.


Architecture is not Agent based but users of system are
modeled as actors.


Knowledge Model
, its
implementation

and modified

algorithms
presented

in thesis are used


Title:
Knowledge
-
based Workflow System for Grid
Applications


Objectives:
To support workflow construction and
execution with Knowledge


Duration:

Sep 2004
-

Feb 2007

Conclusion and Future Work

Bratislava, 12th January 2006

32

Conclusion (1)


The most significant scientific achievements


Agent knowledge model


Applicable in any discrete environment where actors need to be
modeled


Can be expressed by ontology, sets or description logic


Such model was found useful for:


Simple goal oriented agents


Knowledge Management Solution based on Agents (Pellucid)


Experience Management Solution non agent based (EMBET
System)


Development Methodology


Speed up Knowledge based Agent development for concrete
application domains



Bratislava, 12th January 2006

33

Conclusion (2)


The most significant development achievements


Agent Library


Support for OWL based Agent Knowledge Model


Support for XML
-
RPC connection to receive event and send plain XML


Support for Presentation of Ontological Knowledge (RDF/OWL => plain
XML + XSL => HTML)


Support for agent communication using FIPA ACL with OWL and RDQL
as content languages


JADE and Jena Integration


Available on JADE official website to MAS community

(August
-

December 2005
–

314 downloads
)

Bratislava, 12th January 2006

34

Conclusion (3)


Extension of Work for

Experience Management


Model


Algorithms


Projects


Motivation for solving problems in real
Application


Evaluation of Thesis results

Bratislava, 12th January 2006

35

Future work


RAPORT
APVT project (01/2005
-
12/2007): Research and
development of a knowledge based system to support workflow
management in organizations with administrative processes


model and algorithms will be reused and extended



K
-
Wf Grid

EU 6FP RTD IST project (2004
-
2007)


evaluation on more applications, improvement of context detection



NAZOU

SPVV Project (09/2004
-
11/2007): Tools for acquisition,
organization and maintenance of knowledge in an environment of
heterogeneous information resources


OnTeA semantic annotation
–

not directly related but can be used for
context detection

Thank you !

Thank You for you attention

Many Thanks to my supervisor

Many Thanks to my colleagues

Many Thanks to the Reviewers for their helpful and
constructive comments and for reading my thesis