Type 2 fuzzy ontology ahmadchan

A smart coupling of type-2 fuzzy ontology (T2FO) with a multi-agent
system: A novel mechanism to automate the personalized itinerary

Student Name: Syed Ahmad Chan Bukhari

Student Id: 2010214029

Lab: Artificial Intelligence Lab

Supervised by: Prof. Yong-Gi Kim

Department of Computer Science, Gyeongsang National University, Jinju Korea 1

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
• Multi-Agent System
– Terminology, Role, Integration and usage
– Architecture and working
• Architecture of STFO-MAS and its Application to automate the personalized itinerary
– inside decision supported multi-agent pool
– Inside the Natural language query processing
• Experiments and results
– Ontology Evaluation
– Overall system evaluation
– Extracted results
– Graphical efficiency comparison

ST2FO-MAS to automate personlized
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Background and Motivation
• As the internet grows rapidly, millions of web pages are being
added on a daily basis
• Personalized information extraction and intelligent decision making
on it behalf are challenging issues nowadays
• Explosive internet heterogeneity making relevant Info. Extraction
and intelligent decision making more challenging
• 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, uncertain
• Optimal searching not possible by using conventional ways
• Currently users spend hours and hours to find desired information
from internet
• Any solution?
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Past research work
Researchers Research area/ Domain Year Tools and technologies

Yi et al. To represent the Chinese medicines 2010 Ontology, Fuzzy system

Zhai et al. SCM 2009 Ontology

A. Segev et al. Patent search 2010 Ontology

Huiying et al. Enterprise information-retrieval model 2009 NLP, Ontology, AI

Noy et al. FOGA 2001 Fuzzy system, ANN, Ontology

Zhai et al. E-commerce domain 2008 Fuzzy Ontology

hang Shing et al. Diet recommendations for diabetic patient 2011 FML, ONTOLOGY

C.S. Lee To present the computer Go knowledge 2010 Fuzzy system, ontology

Wang, M et al. Automate meetings scheduling 2010 FNL, Ontology, AI

Jaber et al. Customized learning paths in an e-learning platform 2010 MAS, Ontology

S. Yang E-health 2010 MAS, Ontology

Szu-Yin, L et al. Corporate tacit knowledge 2005 Ontology, AI

Jung et al. Indirect alignment between multiple language ontologies. 2011 MAS, Ontology, AI

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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 but with low precision rate and with high
cost
• We provide an end-to-end solution to automate the optimal information
extraction and decision making
• Our system based on: Secure Type-2 Fuzzy Ontology MAS
– Why Type-2 fuzzy system used?
– Why incorporated Type-2 fuzzy system with ontology?
– Why information security important?
– What is Ontology and how can we exploit it?
– What is the co-relation of MAS, NLP with T2FO and optimal information
extraction and decision making?
• Domain of application: Personalized itinerary booking (Why use this
domain???)
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ST2FO-MAS to automate personalized Itinerary
(Problem Intro.)
• Manual air ticket booking : time consuming and laborious
• Easiness of web technology provides opportunity to travel companies to
online their portals
• Thousands of solutions available now
• Passengers spend hours to find acceptable fare
• Travelers are anxiously waiting for solution with personalized outcomes
Problems Proposed Solution

Intensively blurred information Type-2 Fuzzy system
Scattered information resources  T2FO and MAS
Personalized constraints Type-2 Fuzzy ontology
Tour’s operator limitations
Information security based on XML
Increasing Information security
challenges (hacking risks) Type-2 Fuzzy Ontology
Limitation of Fuzzy Information NLP
acquisition techniques MAS,NLP and T2FO
Usability issues Ontology with MAS
Process Automation

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Secure Type-2 Fuzzy Ontology

A quick review of terminologies
Ontology:
The term ontology has its origin in philosophy and has been applied in many
different ways.

1. “An ontology formally represents knowledge as a set of concepts within
a domain, and the relationships between those concepts.”

2. “Formal, explicit specification of a shared conceptualization“

Main Components of Ontology

Individuals: instances or objects (the basic or "ground level" objects)
Classes: sets, collections, concepts, types of objects, or kinds of things.
Attributes: aspects, properties, features, characteristics, or parameters that
objects (and classes) can have
Relations: ways in which classes and individuals can be related to one another

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(A quick review of terminologies)

Common definitions and concepts about type-1 Fuzzy set and type-2
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].

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(A quick review of terminologies)

Type-2 Fuzzy System
• Type-1 or conventional fuzzy logic 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 logic
• Type-2 fuzzy logic 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.

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(Ontology Development)

OUR Proposed formation of Type-2 Fuzzy Ontology Building

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Development of Secure type-2 fuzzy ontology
The anatomy of Type-2 Secured Fuzzy Ontology (Layered
Domain Ontology Development steps
Architecture)

1. Determine the domain
and scope of the
ontology.
2. Consider reusing
existing ontologies.
3. Enumerate important
terms in the ontology.
4. Define the classes and
the class hierarchy.
5. Define the properties of
the classes.
6. Define the facets of the
slots.
7. Create instances.

Language: OWL-2 , RDF and Protégé
Reasoner: Pellet, DeLorean
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• 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 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:

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Secure Type-2 Fuzzy Ontology of Ticket Booking Domain

(Information security)

Why information security important?
• 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

Possible Information security Challenges
• DOS attack on server
• XML content exploit attack (data holders: CDATA,PCDATA, NUMBER)
• X-Path altering attack (also known as XML bomb)

Light Weight solution for content security
• XML security recommendations developed by W3C
•XML digital signature
• XML encryption
• XML key management specification (XKMS)
•security assertion markup language
• XML access control markup language XACML) 14

(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>
<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>
<CipherValue>vHE@#$&&JUIOFdefghj...</CipherValue>
</CipherData>
</EncryptedKey>
</KeyInfo>
<CipherData>
<CipherValue>yyFE%!JJNIcflijnvcthsdrtg...</CipherValue>
</CipherData>
</EncryptedData>
</CustomerInfo>
</owlx:Ontology>

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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 is diverse
 Complex and unstructured
 For automatic information extraction
 For intelligent decision making

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Multi-agent system ( Architecture and working)

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A graphical architecture of STFO-MAS and its Application to automate the
personalized itinerary

2 3

1 4

5
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What's inside decision supported multi-agent pool?

Agent Name Agent Acronym Funcationality

Query Processing Agent QPA  Natural language to query building
 Query Processing
 Query Optimization
Personal Preferences and PPSA  Interaction with personal ontology
Schedule Maintaining Agent  Communication with other agents to rovide the
personal preferences information
 Monitoring the information process and
implementations of user constraints.

Type-2 Fuzzy Inference T2FIA  Crtical decision making based on information
Engine Agent  Remain in touch all the time with PPSA and SBTA
 Responsible for making underlying connection with
fuzzy ontology

Secured Bank Transaction SBTA  Receiving requests for transaction.
Agent  Authentication
 Resorce allocation
 Transaction processing
 Log generation
Ticket reservation Agent TRA  Making connection with travel agency databases
 Finding and reserving of the optimal ticket
 Keep in touch with T2FIA AND PPSA

Multi-agent system schema
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What's Inside the Natural language query processing agent
(QPA)?

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).”

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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:

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System security Evaluator

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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, recall and time
• To exact judge the performance, we requested five volunteer to
help us in experiments.
•The volunteers enquired from the system by using crisp ontology
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:

here ‘ce’ is the total number of records that are extracted from the internet,
and ‘te’ and ‘fe’ represent the true and false elements in the extracted records.
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Experiments and results (Extracted results)

Overall system performance results recoded in the case of the secured type-1 fuzzy ontology.
Total Number of No of False Precision Recall Job
Number of True Elements (fe) Percentage Percentage Completion
Resource Elements (te) (PP) (%) (RC) (%) Time (JCT)
Extracted (Seconds)
Corpus 1 (ce)

Volunteer 1 569 191 378 61.1 74.8 180

Volunteer 2 479 146 333 58.9 76.6 234

Volunteer 3 587 275 312 58.1 68.1 156

Volunteer 4 389 87 302 94.8 81.8 132

Volunteer 5 495 198 297 62.5 71.5 210

Overall system performance results recoded in the case of the secured type-1 fuzzy
ontology.
Total Number Number of No of False Precision Recall Job Completion
of Resource True Elements Elements (fe) Percentage (PP) Percentage (RC) Time (JCT)
Extracted (te) (%) (%) (Seconds)
Corpus 1 (ce)

Volunteer 1 569 311 258 68.8 71.2 228

Volunteer 2 479 292 187 71.9 67.3 258
Volunteer 3 587 496 91 86.5 54.2 286
Volunteer 4 389 278 111 77.9 58.3 305
Volunteer 5 495 267 228 68.5 64.9 315

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Overall system performance results recoded in the case of the secured type-2
fuzzy ontology.
Total Number of No of False Precision Recall Job
Number of True Elements (fe) Percentage Percentage Completion
Resource Elements (te) (PR) (%) (RC) (%) Time (JCT)
Extracted (Seconds)
Corpus 1 (ce)

Volunteer 1 569 437 159 78.2 56.5 336

Volunteer 2 479 337 142 77.2 58.8 319

Volunteer 3 587 530 57 91.1 52.55 422

Volunteer 4 389 279 110 77.9 58.23 357

Volunteer 5 495 391 104 82.6 55.3 467

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Experiments and results (Efficiency Comparison)

Crisp ontology Case

Fuzzy ontology Case

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Experiments and results (Efficiency Comparison)

Type-2 Fuzzy ontology
Case

Combine Efficiency Analysis

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Many Thanks for your Kind attention!

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Type 2 fuzzy ontology ahmadchan

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