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

1

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

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

2 ST2FO-MAS to automate personlized

itinerary

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?


itinerary

Past research work

ST2FO-MAS to automate personlized itinerary

4

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

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???)


itinerary

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

6

Problems Proposed Solution

Intensively blurred information Scattered information resources Personalized constraints Tour’s operator limitations Increasing Information security challenges (hacking risks) Limitation of Fuzzy Information acquisition techniques Usability issues Process Automation

Type-2 Fuzzy system T2FO and MAS Type-2 Fuzzy ontology Information security based on XML Type-2 Fuzzy Ontology NLP MAS,NLP and T2FO Ontology with MAS


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


itinerary

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].

Secure Type-2 Fuzzy Ontology (A quick review of terminologies)


itinerary

Secure Type-2 Fuzzy Ontology (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.

9


10

Secure Type-2 Fuzzy Ontology (Ontology Development)

OUR Proposed formation of Type-2 Fuzzy Ontology Building

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

Development of Secure type-2 fuzzy ontology

11

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.

Domain Ontology Development steps

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


12


• 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:

Secure Type-2 Fuzzy Ontology of Ticket Booking Domain 13


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

Secure Type-2 Fuzzy Ontology (Information security)

14

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)


15

<? 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>

Secure Type-2 Fuzzy Ontology (Information security: Application scheme)

• 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

16

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



itinerary

Multi-agent system ( Architecture and working)

18

5

2 3

1 4

8

7

6

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


itinerary

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

Schedule Maintaining Agent

PPSA Interaction with personal ontology

Communication with other agents to rovide the

personal preferences information

Monitoring the information process and

implementations of user constraints.

Type-2 Fuzzy Inference

Engine Agent

T2FIA Crtical decision making based on information

Remain in touch all the time with PPSA and SBTA

Responsible for making underlying connection with

fuzzy ontology

Secured Bank Transaction

Agent

SBTA Receiving requests for transaction.

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


itinerary

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).”


itinerary

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:


22


System security Evaluator


itinerary


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.


24

Total

Number of

Resource

Extracted

Corpus 1 (ce)

Number of

True

Elements (te)

No of False

Elements (fe)

Precision

Percentage

(PP) (%)

Recall

Percentage

(RC) (%)

Job

Completion

Time (JCT)

(Seconds)

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.

Experiments and results (Extracted results)


Total Number

of Resource

Extracted

Corpus 1 (ce)

Number of

True Elements

(te)

No of False

Elements (fe)

Precision

Percentage (PP)

(%)

Recall

Percentage (RC)

(%)

Job Completion

Time (JCT)

(Seconds)

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


25



Total

Number of

Resource

Extracted

Corpus 1 (ce)

Number of

True

Elements (te)

No of False

Elements (fe)

Precision

Percentage

(PR) (%)

Recall

Percentage

(RC) (%)

Job

Completion

Time (JCT)

(Seconds)

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


26

Experiments and results (Efficiency Comparison)

Crisp ontology Case

Fuzzy ontology Case


27

Experiments and results (Efficiency Comparison)

Type-2 Fuzzy ontology Case

Combine Efficiency Analysis


28

References

1. A. Segev, J. Kantola, Patent Search Decision Support Service, In: Proceedings of Seventh International Conference on Information Technology, 2010, pp. 568-573.

2. A. Vorobiev, J. Han, Security Attack Ontology for Web Services, Semantics, In: Proceedings of Second International Conference on Knowledge and Grid, 2006, pp. 42-49.

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 (2010), pp. 374-395

6. C. Lee, M. Wang, M. Wu, C. Hsu; Y. Lin, S. Yen , A type-2 fuzzy personal ontology for meeting scheduling system, In: Proceeding of International Conference on Fuzzy Systems, 2010 , pp. 1-8

7. C.J Su, C.Y Wu, JADE implemented mobile multi-agent based, distributed information platform for pervasive health care monitoring , Applied Soft Computing Journal 11 (2011), 315-325.

8. C.I. Nyulas, M.J. O'Connor, S.W. Tu, D.L. Buckeridge, A. Okhmatovskaia, M.A. Musen, An Ontology-Driven Framework for Deploying JADE Agent Systems, IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2008, pp. 573-577.

9. C. Lee, C. Jiang, T. Hsieh, A genetic fuzzy agent using ontology model for meeting scheduling system, Information Sciences 176 (2006) 1131-1155 10. C. Lee, M. Wang, G. Acampora, C. Hsu, and H. Hagras, Diet assessment based on type-2 fuzzy ontology and fuzzy markup language, Int. J. Intell. Syst., 25

(2010) 1187-1216. 11. C. S. Lee, M. H. Wang. Z. R. Yang, Y. J. Chen, H. Doghmen, and O. Teytaud, FML-based type-2 fuzzy ontology for computer Go knowledge representation,

In: Proceeding of International Conference on System Science and Engineering (ICSSE 2010), 2010, pp. 63-68. 12. C.D. Maio,G. Fenza, V. Loia, S. Senatore , Towards an automatic fuzzy ontology generation," In: Proceedings of IEEE International Conference on fuzzy

system,2009, pp.1044-1049. 13. C.D. Maio,G. Fenza, V. Loia, S. Senatore, Hierarchical web resources retrieval by exploiting Fuzzy Formal Concept Analysis, Information Processing &

Management Available online 26 May 2011, ISSN 0306-4573. 14. D.H. Fudholi, N. Maneerat, R. Varakulsiripunth, Y. Kato, Application of Protégé, SWRL and SQWRL in fuzzy ontology-based menu recommendation,

International Symposium on Intelligent Signal Processing and Communication Systems, 2009, pp. 631-634. 15. D.Wu, J.M. Mendel, Uncertainty measures for interval type-2 fuzzy sets, Information Sciences, 177 (2007) 5378-5393. 16. E. Gatial, Z. Balogh, M. Ciglan, L. Hluchy, Focused web crawling mechanism based on page relevance, In: Proceedings of (ITAT 2005) information

technologies applications and theory, 2005, pp. 41–45 17. F. Abdoli, M. Kahani, Ontology-based distributed intrusion detection system,In: Proceedings of 14th International Computer Conference, 2009, pp. 65-

70.

18. F. Bobillo, U. Straccia, Fuzzy Ontology Representation using OWL 2, International Journal of Approximate Reasoning (2011) 1-36. 19. F. Bobillo, M. Delgado, J. Gomez-Romero, DeLorean: a reasoner for fuzzy OWL 1.1, In: Proceedings of 4th International Workshop on Uncertainty

Reasoning for the Semantic Web (URSW 2008), 2008, pp. 35-44. 20. F.N Natalya, LM Deborah, Ontology development 101: a guide to creating your first ontology.

http://protege.stanford.edu/publications/ontology_development/ontology101-noy-mcguinness.htm 21. H. Gao, J. Zhao, Q. Yin, J. Wang, Ontology-based enterprise information retrieval model, IEEE International Conference on Grey Systems and

Intelligent Services, 2009, pp. 1326-1330. 22. H. Cunningham, Y. Wilks, R. J. Gaizauskas, GATE, a General Architecture for Text Engineering. Computers and humanities (2002), 1057-1060. 23. J. Lakshmipathi, T. Chithralekha, VIHARI: An Ontology Sevice Driven Multi-Agent System for Air Ticket Booking, in: Proceedings of Advanced

Computing and Communications, 2007, pp. 675-680. 24. J. Zhai, Q. Wang, M. Lv, Application of Fuzzy Ontology Framework to Information Retrieval for SCM, In: Proceedings of International Symposiums

on Information Processing (ISIP), 2008, pp. 173-177 25. J. Zhai, L. Shen,Y. Liang, J. Jiang, Application of Fuzzy Ontology to Information Retrieval for Electronic Commerce, In: Proceedings of International

Symposium on Electronic Commerce and Security, 2008, pp. 221-225. 26. J. Zhai, M. Lv, J. Li, Q. Wang, Knowledge modeling for fuzzy systems based on ontology, In: Proceedings of Control and Decision Conference, 2008,

pp. 3393-3397. 27. J.J. Jung, Exploiting multi-agent platform for indirect alignment between multilingual ontologies: A case study on tourism business, Expert Systems

with Applications 38 (2011), 5774-5780. 28. J.M Mendel, R.I. John, F. Liu, Interval Type-2 Fuzzy Logic Systems Made Simple, IEEE Transactions on Fuzzy Systems 14 (2006) 808-821. 29. J.R. Chen, S.R. Wolfe, S.D. Wragg, A distributed multi-agent system for collaborative information management and sharing, in: Proceedings of the

ninth international conference on Information and knowledge management, 2000, pp.382-388. 30. J.M. Mendel, Advances in type-2 fuzzy sets and systems, Information Sciences 177 (2007) 84-110 31. J. Lee, K. Whang, Secure query processing against encrypted XML data using Query-Aware Decryption, Information Sciences, 176 (2006) 1928-

1947 32. J.E Bouhdidi, M. Ghailani, O. Abdoun and A. Fennan, A New Approach based on a Multi-ontologies and Multi-agents System to Generate

Customized Learning Paths in an E-Learning Platform, International Journal of Computer Applications, 12(2010), 1-6. 33. K. Sim, P.Wong, Toward agency and ontology for web-based information retrieval, IEEE Transactions on Systems, Man, and Cybernetics, Part C:

Applications and Reviews, 34(2004), 257-269. 34. L.A. Zadeh, Fuzzy Sets, Information Control 8 (1965) 338-353. 35. M.S. Kim, K.Y. Whang, J.G. Lee, and M.J. Lee, n-Gram/2 L: A Space and Time Efficient Two-Level n-Gram Inverted Index Structure, in VLDB,

Trondheim (2005) 325-336. 36. M. Cremonini, S. De Capitani di Vimercati, E. Damiani, P.Samarati, An XML-based approach to combine firewalls and web services security

specifications, XML Security (2003) 69-78 37. M.H. Wang, C.S. Lee, K.L. Hsieh, C.Y Hsu, G. Acampora, C.C Chang , Ontology-based multi-agents for intelligent healthcare applications, Journal of

Ambient Intelligence and Humanized Computing, 1(2010), 1-21. 38. N.F. Noy, D.L. McGuinness, Ontology Development 101: A Guide to Creating Your First Ontology, Stanford Knowledge Systems Laboratory

Technical Report KSL-01-05 and Stanford Medical Informatics Technical Report, 2001, SMI-2001-0880. 39. N. Shadbolt, W. Hall, T.B. Lee, The semantic web revisited, Journal of Intelligent System 21(2006) 96–101. 40. N.N. Karnik, J.M. Mendel, Operations on type-2 fuzzy sets, Fuzzy Sets and Systems 122, (2001) pp 327-348.


29

References

http://protege.stanford.edu/publications/ontology_development/ontology101-noy-mcguinness.htm





http://www.lamsade.dauphine.fr/mcda/biblio/Author/ZADEH-LA.html

http://www.lamsade.dauphine.fr/mcda/biblio/Author/ZADEH-LA.html


30

References

41. P. Yan, Y. Zhao, C. Sanxing, and Ontology-Based Information Content Security Analysis, In: Proceedings of Fifth International Conference on Fuzzy Systems and Knowledge Discovery, 2008, pp. 479-483.

42. P.D Meo, A. Garro, G. Terracina, D. Ursino, Personalizing learning programs with X-Learn, an XML-based, 'user-device' adaptive multi-agent system, Information Sciences 177(2007) 1729-1770.

43. Q.T Tho, S.C Hui, A.C. Fong, T.H Cao, Automatic fuzzy ontology generation for semantic Web, IEEE Transactions on Knowledge and Data Engineering 18 (2006) 842-856.

44. Q. Guo, M. Zhang, A novel approach for multi-agent-based Intelligent Manufacturing System, Information Sciences 179 (2009) 3079-3090.

45. R.M Sachs, D.W Brown, J. DeLong, A first step on the path to automated flight reservations , In: Proceeding of Third IEEE Workshop on Interactive Voice Technology for Telecommunications Applications,1996, pp. 61-64

46. R. Azevedo, E. Dantas, F. Freitas, C. Rodrigues, M.J. Almeida, W. Veras, R. Santosyi, An Autonomic Ontology-Based Multiagent System for Intrusion Detection in Computing Environments, International Journal for Infonomics (IJI) 3 (2011) 182 – 189.

47. R. Subhashini, V.J.S Kumar, Shallow NLP techniques for noun phrase extraction, In: Proceeding of Trendz in Information Sciences & Computing (TISC), 2010 , pp.73-77.

48. S. Nasrolahi, M. Nikdast, M. Boroujerdi, The semantic web: a new approach for future world wide web, In: Proceedings of World Academy of Science, Engineering and Technology, 2009, pp. 1149-1154

49. S. Yi, Z. Dezheng, C. Li, Fuzzy ontology constructing and its application in traditional Chinese medicine, In: Proceedings of International Conference on Intelligent Computing and Intelligent Systems (ICIS), 2010 pp. 592-595

50. S. Bechhofer, F.V Harmelen, J. Hendler, I. Horrocks,D.L McGuinness, P.F Patel-Schneider,L.A Stein, OWL Web Ontology Language Reference, 2004, W3C Recommendation http ://www.w3.org/TR/owl-ref.

51. S. Beji, N. Kadhi, Security Ontology Proposal for Mobile Applications, In: Proceedings of Tenth International Conference on Mobile Data Management: Systems, Services and Middleware, 2009, pp. 580-587.

52. S. Yang, L. Szu-Yin, L. Shih-Neng, S. Cheng, and V. Soo, An ontology-based multi-agent platform for patent knowledge management, International Journal of Electronic Business Management, 3(2005), 181-192

53. W. Ying, J.S. Wimalasiri, P. Ray, S. Chattopadhyay and C. S. Wilson, An ontology driven Multiagent approach to integrated e-Health systems, International Journal of E-Health and Medical Communications, 1(2010), 28-40

Many Thanks for your Kind attention!


itinerary

Type 2 fuzzy ontology ahmadchan

Education

Transcript of Type 2 fuzzy ontology ahmadchan