Type-2 Fuzzy Ontology

Contents

Background and motivation
Past research work
Proposed solution
ST2FO-MAS to automate personalized Itinerary
(Problem Intro.)
Secure Type-2 Fuzzy Ontology
Secure Type-2 Fuzzy Ontology (A quick review of
terminologies)
Type-1 Fuzzy system
Type-2 Fuzzy system
Secure Type-2 Fuzzy Ontology Development
Crisp ontology development
Type-1 Fuzzy ontology Development
Type-2 Fuzzy ontology development
2
Multi-Agent System

Related Publications

1. Ahmad C. Bukhari, Yong-Gi Kim, “Integration of Secure Type-2 Fuzzy Ontology with Multi-
agent Platform: A proposal to automate the Personalized Flight Ticket Booking Domain ”
Journal of Information sciences (SCI Index) Impact factor 2.9 For Tracking :
http://dx.doi.org/10.1016/j.ins.2012.02.036

2. Ahmad C. Bukhari ,Yong-Gi Kim, “Ontology-assisted automatic precise information extractor
for visually impaired inhabitant” Journal of Artificial Intelligence Review 2011
http://dx.doi.org/10.1007/s10462-011-9238-6 (SCI index)

3. Ahmad C. Bukhari , Yong-Gi Kim, "Exploiting the Heavyweight Ontology with Multi -Agent
System Using Vocal Command System: A Case Study on E-Mall", IJACT : International
Journal of Advancements in Computing Technology, Vol. 3, No. 6, pp. 233 ~ 241, 2011
(SCIE, Scopus Index)

4. Ahmad C. Bukhari, Yong-Gi Kim, “Incorporation of Fuzzy Theory with Heavyweight Ontology
and Its Application on Vague Information Retrieval For Decision Making” International
Journal of Fuzzy Logic and Intelligent System, vol.11, no.3, September 2011, pp, 171-17
Http://dx.doi.org/10.5391/IJFIS.2011.11.3.171 (KISTI, ACOMS, SCIE Index)

3

Background and Motivation

As the internet grows rapidly, millions of WebPages are being
added on a daily basis
Personalized information extraction and intelligent decision
making on it behalf are becoming challenging issues
Explosive internet heterogeneity makes the relevant Info.
extraction and intelligent decision making more tricky
Search engines are used commonly to find information
Conventional mechanism of searching: keywords and directory
structure
Most of the data on internet is in imprecise and uncertain format
Optimal searching not possible by using conventional ways
Currently users spend hours and hours to find desired
information from internet
Any solution?

4

Past research work

5

Proposed solution

Researchers proposed several solution but mostly failed with time, due
to diverse and fatally vague nature of web data
Some solutions found working nevertheless with low precision rate and
their performance decreasing drastically
We present an end-to-end solution to automate the optimal information
extraction and decision making
Underlying technologies of our system are: Type-2 Fuzzy Ontology,
MAS, NLP, Info. Security
Why we use type-2 fuzzy system?
Why we incorporated Type-2 fuzzy system with ontology?
Why the information security is so important?
What is the ontology and how can we exploit it for info. extraction?
What is the relation among MAS, NLP and T2FO to extract the optimal
information and for appropriate and timely decision making?

6

ST2FO-MAS to automate personalized Itinerary
(Ongoing challenges and proposed solutions)

7

ST2FO-MAS to automate personalized Itinerary (Problem Intro.)

Manual air ticket booking : time consuming and laborious
The domain is rife with uncertainties and with complex linguistic
terminologies
Thousands of solutions available now but mostly work for specific
airlines or for specific routes
Generally, passengers spend hours to find acceptable fare
A complete process (selection, reservation, booking with
transaction) requires full-time use involvement
Travelers are anxiously waiting for automatic solution with
personalized outcomes

8


A quick review of terminologies
Ontology:
An ontology is a branch of
metaphysics that focuses on the
study of existence.
“An ontology is an explicit and
formal specification of a shared
conceptualization of a domain, which
is machine readable and human 9

Common definitions and(A quick review of terminologies) set and type-2
concepts about type-1 Fuzzy
Type-1 Fuzzy system
• The fuzzy set theory was introduced by Lotfi Zadeh in 1965 to deal with vague
and imprecise concepts.
• In classical set theory, elements either belong to a particular set or they don’t
belong.
• However, in fuzzy set theory the association of an element with a particular set
lies between ‘0’ and ‘1’ which is called degree of association or membership
degree. A fuzzy set can be defined as:
Definition 1: A fuzzy set ‘s’ over universe of discourse ‘X’ can be defined by its
membership function µ_s which maps element ‘x’ to values between [0,1].

10

(A quick review of terminologies)
Type-2 Fuzzy System
Type-1 fuzzy system or conventional fuzzy system can handle the uncertainty at
certain level.
Some Fact

vagueness are the vital parts of any real-time system
Uncertainty and vagueness is increasing continuously due to heterogeneity.
How to handle the extensive blurred information?

Solution: Type-2 Fuzzy system
Type-2 fuzzy system is the extended version of classical fuzzy set theory.
In type-1 fuzzy set theory, the membership values are crisp, while type-2 fuzzy
systems have fuzzy membership values.

11

(Ontology Development)
Proposed formation of Type-2 Fuzzy Ontology Building

12

Development of Secure type-2 fuzzy ontology
• Fuzzy ontology can be defined in the form of fuzzy sets.
• Let be fuzzy class in universe of discourse µ then

and the relationship between two ontology classes are fuzzy relation

• Annotation rule feature of protégé is used to define fuzzy concept in
fuzzy ontology
• Manual process of annotation adding is a complex and error pruning
• Protégé fuzzy OWL tab helps us to make this process handy
• A class of cheap ticket can be described in to fuzzy form as:

• Similarly very cheap ticket can be expressed as:

13

p Domain Ontology Development steps Source: Ahmad C. Bukhari,
Yong-Gi Kim, “Incorporation
of Fuzzy Theory with
1.Determine the domain and scope of the ontology. Heavyweight Ontology
and Its Application on Vague
2.Consider reusing existing ontologies. Information Retrieval For
Decision Making”
3.Enumerate important terms in the ontology. International
Journal of Fuzzy Logic and
4.Define the classes and the class hierarchy.
Intelligent System, vol.11,
5.Define the properties of the classes.
no.3, September 2011, pp,
171-17
6.Define the facets of the slots.

7.Create instances.

Language: OWL-2 , RDF and Protégé
Reasoner: DL-reasoner, Pellet, DeLorean
14


The anatomy of Type-2 Secured Fuzzy Ontology (Layered Architecture) 15

Development of Secure type-2 fuzzy ontology (Internal Schema)
Secure Type-2 Fuzzy Ontology of Ticket Booking Domain

Source: Ahmad C. Bukhari ,Yong-Gi Kim, “Ontology-assisted automatic precise information extractor 16
for visually impaired inhabitant” Journal of Artificial Intelligence Review

1
Development of Secure type-2 fuzzy ontology (Fuzz-owl Plugin) 2

3

Readers interest
Source: Ahmad C. Bukhari,
Yong-Gi Kim, “Incorporation
of Fuzzy Theory with
Heavyweight Ontology
and Its Application on Vague
Information Retrieval For
Decision Making”
International
Journal of Fuzzy Logic and
Intelligent System, vol.11,
no.3, September 2011, pp,
171-17

17

Why information security important? security)
(Information
• Information is the most valuable assets of any organization.
• Nowadays, secure information has become a strategic issue for online businesses.
• In ontology, all kind of information is shared in plain text format.
• This raises the issues of information leakage, altering and deletion of information contents
• Information security can be achieved to increase the satisfaction level of authentication,
authorization, integrity and confidentiality.
• We used XML security recommendation to achieve this recommendation.

XML security recommendations developed by W3C

• XML digital signature
• XML encryption
• XML key management specification (XKMS)
• Security assertion markup language (SAML)
• XML access control markup language (XACML)
Possible Information security Challenges
• DOS attack on server
• XML content exploit attack (data holders: CDATA,PCDATA, NUMBER)
• X-Path altering attack
18

(Information security: Application scheme)
<? XML version="1.0"?>
<! DOCTYPE Ontology [
<! ENTITY xsd "http://www.w3.org/2001/XMLSchema#" > ]>
<owlx:Ontology owlx:name="http://www.ailab.gnu.ac.kr/t2fo"
xmlns:owlx="http://www.w3.org/2003/05/owl-xml">
<CustomerInfo xmlns='http://www.ailab.gnu.ac.kr/st2fo-mas/person_ontology'>
<Name>ahmad chan</Name>
Public Key encryption algorithm
<EncryptedData Type='http://www.w3.org/2001/04/xmlenc#Element'
xmlns='http://www.w3.org/2001/04/xmlenc#'>
<EncryptionMethod Algorithm='http://www.w3.org/2001/04/xmlenc#tripledes-cbc'/>
<KeyInfo xmlns='http://www.w3.org/2000/09/xmldsig#'>
<EncryptedKey xmlns='http://www.w3.org/2001/04/xmlenc#'>
<EncryptionMethod Algorithm='http://www.w3.org/2001/04/xmlenc#rsa-1_5'/>
<KeyInfo xmlns='http://www.w3.org/2000/09/xmldsig#'>
<KeyName>white tiger</KeyName>
</KeyInfo>
<CipherData> XML Data level encryption
<CipherValue>vHE@#$&&JUIOFdefghj...</CipherValue>
</CipherData>
</EncryptedKey>
</KeyInfo>
<CipherData>
<CipherValue>yyFE%!JJNIcflijnvcthsdrtg...</CipherValue>
</CipherData>
</EncryptedData>
</CustomerInfo>
</owlx:Ontology>

Code view of W3C XML security recommendations 19

Multi-agent system ( Terminology, Role, Integration and usage)
Diversity and complexity factors are increasing day by day in modern
software applications.
The multi-Agent system is considered an efficient technology in the
development of distributed systems.
A multi-Agent system is basically the group of interconnected agents, in
which each agent works autonomously while sharing information.
An agent is a bunch of code which is designed to perform a specific
task on the behalf of its user.
Why we used MAS?
Our domain was diverse

Complex and unstructured

For automatic information extraction

For intelligent decision making

20

A graphical architecture of STFO-MAS and its Application to
2 3
automate the personalized itinerary 1 Client GUI Panel

1 4
2 Query Processing Agent

5
3 Search engines Pool
6

4 Data Repository

7
5 Ontology Bases Crawler
8

6 Type-2 Fuzzy Ontologies

7 MAS Pool

8External Data repositories
21

What's inside decision supported multi-agent pool?

Multi-agent system schema

22

Multi-agent system
Import Jade.core.agent; //Package Importing ( Algorithm and working)
Public class TicketBookMAS
{
StartupQPA()
{
getInput(){ // taking input from user }
//Intialization stage
g.Action()
{
NLP Processing ();
QueryOptimization();
Webcrawling();
}
R=Get.Result();
mkConnect(IA) // Make connection with inference agent
Start thread 1
Delay 1000 mili second;
Startup IA()
{
Mkconnect(PPRA && QPA);
Inferencing(); //optimal ticket selection based on information provided by QPA
StoreResults();
Start thread 2
Thread1 stop //store information and break the process
Startup (SBTA && TRA)
{
//Initialization of SSL (SECURE SOCKET LAYER)
Autoauthenticate();
Q= resultant input=Optimal ticket;
RequestForReservation(Iternary Number like 152895623462);
//for bank payment , taking user concern first and then sharing bank crediential
through secure XML channel
Reservation Complted; //information displayed and stored and closing all
connection
Terminate thread 1
//Close all processs
}}} 23

Experiments
What's Inside the query processing agent (QPA)?

de QPA, we perform four steps after receiving natural language query (NLQ) from client

1.Tokenization
2.word category disambiguation

3.shallow parsing process

4.DL-query generation

I(noun) want to(preposition) go(verb) from(preposition)
Seoul(noun) to London(noun) to attend(preposition) a
meeting(verb) . The meeting will be held afternoon (noun,
adjective), so I want to take (verb) vegetables (noun) in
lunch (noun). Please book (verb) a ticket (noun) of
economy class (noun+ adjective) with cheap rate (noun+
adjective) and minimum delay (noun+ adjective).”
24

QPA Functionality algorithm

Tokenization

Query processing and optimization

Initial data filtration and storage stage

Second Optimization Stage

25

Experiments and results
Ontology Evaluation

• We evaluated the ontology after completion of each phase of T2FO development to
measure the efficiency
•We used Manchester OWL-2 syntax of DL-query to evaluate the efficiency of ontology

Some queries results are:

DL-Query

Ontology Classes

26
Possible results

System security Evaluator
• We developed a module to evaluate the overall system security.

• We added the malicious code and to engage the system

• system generated the beeps and paused all the operation

27

Overall system evaluation
• Information system can be categorized on the basis of its effectiveness.
• There are some known ways to define the efficiency of an information
system, such as the precision (PR), recall(RC) and time
• To exact judge the performance, we requested five volunteers to
help us in experiments.
• The volunteers enquired from the system by using crisp ontology, type-1
fuzzy and Type-2 fuzzy ontology.
• we noted the time, precision and recall in each mode.
• Mathematically, the precision and recall can be expressed as the following:

‘ce’ is the total number of records that are extracted from the internet,
‘te’ and ‘fe’ represent the true and false elements in the extracted records.
28

Results (Extracted results)

Overall system performance results recoded in the case of the secured type-1 fuzzy ontology.

Overall system performance results recoded in the case of the secured type-1 fuzzy
ontology.

29

Results

Overall system performance results recoded in the case of the secured type-2
fuzzy ontology.

30

Results (Efficiency Comparison)

Crisp ontology Case

Precision

Type-1
Fuzzy ontology Case

31

Type-2
Fuzzy
ontology Case

Continuously Precision increasing

32

References
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3.A.C. Bukhari, Y.G Kim, Exploiting the Heavyweight Ontology with Multi-Agent System Using Vocal Command System: A Case Study on E-Mall, International

Journal of Advancements in Computing Technology 3(2011) 233-241.
4.A.C. Bukhari, Y.G Kim, Ontology-assisted automatic precise information extractor for visually impaired inhabitants, Artificial Intelligence Review (2005) Issn:

0269-2821.
5.C. Lee, M. Wang, H. Hagras, A Type-2 Fuzzy Ontology and Its Application to Personal Diabetic-Diet Recommendation, IEEE Transactions on Fuzzy Systems 18

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OWL 2, International Journal of
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Many Thanks for your Kind attention!

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Type-2 Fuzzy Ontology

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