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 systemSecure Type-2 Fuzzy Ontology Development

Crisp ontology development

Type-1 Fuzzy ontology Development

Type-2 Fuzzy ontology developmentMulti-Agent System

Terminology, Role, Integration and usage

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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)

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?

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Past research work

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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 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?

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

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

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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


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

understandable.”9

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].


(A quick review of terminologies)

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(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.

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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

• 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:


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Crisp Domain Ontology Development steps

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

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.

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

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


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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 for visually impaired inhabitant” Journal of Artificial Intelligence Review

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Development of Secure type-2 fuzzy ontology (Fuzz-owl Plugin)12

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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

Readers interest

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• 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.


(Information security)

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

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)

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<? 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>


(Information security: Application scheme)

XML Data level encryption

Code view of W3C XML security recommendations

Public Key encryption algorithm

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

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Multi-agent system ( Terminology, Role, Integration and usage)

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A graphical architecture of STFO-MAS and its Application to

automate the personalized itinerary 1

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Query Processing Agent

Client GUI Panel

Search engines Pool

Data Repository

Ontology Bases Crawler

Type-2 Fuzzy Ontologies

MAS Pool

External Data repositories

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What's inside decision supported multi-agent pool?

Multi-agent system schema

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Multi-agent system ( Algorithm and working)Import Jade.core.agent; //Package ImportingPublic class TicketBookMAS{StartupQPA(){getInput(){ // taking input from user }//Intialization stageg.Action(){NLP Processing ();QueryOptimization();Webcrawling();}R=Get.Result();mkConnect(IA) // Make connection with inference agentStart thread 1Delay 1000 mili second;Startup IA(){Mkconnect(PPRA && QPA);Inferencing(); //optimal ticket selection based on information provided by QPAStoreResults();Start thread 2Thread1 stop //store information and break the processStartup (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 channelReservation Complted; //information displayed and stored and closing all connection Terminate thread 1 //Close all processs}}}

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ExperimentsWhat's Inside the 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).”

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

1.Tokenization2.word category disambiguation3.shallow parsing process4.DL-query generation

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QPA Functionality algorithm

Tokenization

Query processing and optimization

Initial data filtration and storage stage

Second Optimization Stage

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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:

DL-Query

Ontology Classes

Possible results

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Experiments and resultsSystem 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

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Experiments and results

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:

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

Results (Extracted results)


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Results


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Results (Efficiency Comparison)

Crisp ontology Case

Type-1 Fuzzy ontology Case

Precision

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Continuously Precision increasing

Type-2 Fuzzy ontology Case

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References

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Many Thanks for your Kind attention!

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type-2 fuzzy ontology

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