Intelligent Web Application

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Intelligent Web

Applications (Part 1)

Course Introduction

Vagan Terziyan

AI Department, Kharkov National University of Radioelectronics /

MIT Department, University of Jyvaskyla

[email protected] ; [email protected]

http://www.cs.jyu.fi/ai/vagan/index.html

+358 14 260-4618

Vrije Universiteit Amsterdam, Fall 2002


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Contents

Course Introduction

Lectures and Links

Course Assignment

Examples of course-related

research


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Course (Part 1) Formula:

Web Personalization + Web Mining +

+ Semantic Web + Intelligent Agents == Intelligent Web Applications

- Why ?

- To be able to intelligently utilise huge, rich and sharedweb resources and services taking into account

heterogeneity of sources, user preferences and mobility.

- What included ?

- Introduction to Web content management. Web content personalization.Filtering Web content. Data and Web mining methods. Multidatabase mining.

Metamodels for knowledge management. E-services and their management in

wired and wireless Internet. Intelligent e-commerce applications and mobility

of users. Information integration of heterogeneous resources.


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Practical Information

9 Lectures (2 x 45 minutes each, in English)during period 28 October - 15 November

according to the schedule;

Course slides:available online plus hardcopies; Practical Assignment (make PowerPoint

presentation based on a research paper and send

electronically to the lectureruntil 10 December); Exam - there will be no exam. Evaluation mark

for this part of the course will be given based on

the Practical Assignment


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Introduction:

Semantic Web - new Possibilities for

Intelligent web Applications


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Motivation for Semantic Web

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Before Semantic Web

Web content

UsersCreatorsWW

and

Beyond

8

Semantic Web Structure

Semantic

AnnotationsOntologies Logical Support

Languages ToolsApplications /

Services

Web content

UsersCreatorsWW

and

Beyond

Semantic

eb


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Semantic Web Content: New Users

Semantic

AnnotationsOntologies Logical Support

Languages ToolsApplications /

Services

Web content

UsersCreatorsWWW

and

Beyond

Semantic

Web

Semantic Webcontent

UsersSemanticWeb and

Beyond

Creatorsapplications

agents


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Some Professions around Semantic Web

Content

Agents Annotations

Ontologies

Software engineers

Ontology engineers

Web designers

Content creators

Logic, Proof

and Trust

AI Professionals

Mobile Computing

Professionals


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Semantic Web: Resource Integration

Shared

ontology

Web resources /

services / DBs / etc.

Semantic

annotation


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What else Can be Annotated

for Semantic Web ?Web resources /

services / DBs / etc.

Shared

ontology

Web users(profiles,

preferences)

Web access

devices

Web agents /applications

External world

resources


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Word-Wide Correlated Activities

Semantic Web

Grid Computing

Web Services

Agentcities

Agentcities is a global, collaborative effortto construct an open network of on-line systems

hosting diverse agent based services.

WWW is more and more used for application to application communication.

The programmatic interfaces made available are referred to as Web services.

The goal of the Web Services Activity is to develop a set of

technologies in order to bring Web services to their full potential

FIPA

FIPA is a non-profit organisation aimed

at producing standards for the interoperation

of heterogeneous software agents.

Semantic Web is an extension of the current

web in which information is given well-defined

meaning, better enabling computers and people

to work in cooperation

Wide-area distributed computing, or "grid technologies,

provide the foundation to a number of large-scale efforts

utilizing the global Internet to build distributed computing

and communications infrastructures.


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University of Jyvaskyla Experience:

Examples of Related Courses

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Digitaalisen median erityiskysymyksi (2 ov)seminaarin aihepiiri:

Semanttinen web

Lecturer: Airi Salminen

University of Jyvaskyla, CS & IS Department, Spring 200218

Structured Electronic Documentation

Lecturer: Matthieu Weber

University of Jyvaskyla, MIT Department, Fall 2001, 2002

[email protected]


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IWA Course (Part 1): Lectures


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Lecture 1: Web Content Personalization Overview

http://www.cs.jyu.fi/ai/vagan/Personalization.ppt


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Lecture 2: Collaborative Filtering

http://www.cs.jyu.fi/ai/vagan/Collaborative_Filtering.ppt


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Lecture 3: Dynamic Integration of Virtual Predictors

http://www.cs.jyu.fi/ai/vagan/Virtual_Predictors.ppt


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Lecture 4: Introduction to Bayesian Networks

http://www.cs.jyu.fi/ai/vagan/Bayes_Nets.ppt


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Lecture 5: Web Mining

http://www.cs.jyu.fi/ai/vagan/Web_Mining.ppt


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Lecture 6: Multidatabase Mining

http://www.cs.jyu.fi/ai/vagan/MDB_Mining.ppt


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Lecture 7: Metamodels for Managing Knowledge

http://www.cs.jyu.fi/ai/vagan/Metamodels.ppt


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Lecture 8: Knowledge Management

Making Personal Knowledge Available to Others and

Dealing with Knowledge Taken from Multiple Sources

- are among the basic abilities of an Intelligent Agent

http://www.cs.jyu.fi/ai/vagan/Knowledge_Management.ppt


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Lecture 9: E-Services in Semantic Web

http://www.cs.jyu.fi/ai/vagan/E-Services.ppt


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IWA Course (Part 1): Practical

Assignment


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Practical assignment in brief

Students are expected to select one of below

recommended papers, which is not already

selected by some other student, register his/herchoice from the Course Assistant and make

PowerPoint presentation based on that paper.

The presentation should provide evidence that a

student has got the main ideas of the paper, is

able to provide his personal additional

conclusions and critics to the approaches used.


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Evaluation criteria for practical

assignment

Content and Completeness;

Clearness and Simplicity;

Discovered Connections to IWA Course

Material;

Originality, Personal Conclusions and Critics; Design Quality.


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Format, Submission and Deadlines

Format: PowerPoint ppt. (winzip encodingallowed), name of file is students family name;

Presentation should contain all references to the

materials used, including the original paper;

Deadline - 10 December 2002;

Files with presentations should be sent by e-mail

to Vagan Terziyan ([email protected] [email protected]);

Notification of evaluation - until 15 December.


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Papers for Practical Assignment (1)

Paper 1:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_1_P.pdf Paper 2:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_2_P.pdf

Paper 3:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_3_CF.ps

Paper 4:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_4_CF.pdf

Paper 5:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_5_MW.pdf

Paper 6:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_6_BN.ps

Paper 7:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_7_BN.pdf

Paper 8:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_8_MM.pdf


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Papers for Practical Assignment (2)

Paper 9: http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_9_WM.ps Paper 10:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_10_WM.pdf

Paper 11:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_11_III.pdf

Paper 12:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_12_III.pdf

Paper 13:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_13_KM.pdf

Paper 14:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_14_ES.pdf

Paper 15:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_15_MDB.pdf

Paper 16:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_16_MDB.pdf


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University of Jyvaskyla Experience:

Examples of Course-Related Research


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Mobile Location-Based Service

in Semantic Web

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M-Commerce LBS systemhttp://www.cs.jyu.fi/~mmmIn the framework of the Multi Meet Mobile

(MMM) project at the University of Jyvskyl,

a LBS pilot system, MMM Location-based

Service system (MLS), has been developed.

MLS is a general LBS system for mobile

users, offering map and navigation across

multiple geographically distributed services

accompanied with access to location-based

information through the map on terminals

screen. MLS is based on Java, XML and uses

dynamic selection of services for customers

based on their profile and location.

Virrantaus K., Veijalainen J., Markkula J.,Katasonov A., Garmash A., Tirri H., Terziyan V.,

Developing GIS-Supported Location-Based

Services, In: Proceedings of WGIS 2001 - First

International Workshop on Web Geographical

Information Systems, 3-6 December, 2001, Kyoto,

Japan, pp. 423-432.

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Adaptive interface for MLS client

Only predicted services, for the customer with known profile

and location, will be delivered from MLS and displayed at

the mobile terminal screen as clickable points of interest

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Route-based personalization

Static Perspective Dynamic Perspective

M bil T i M


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Mobile Transactions Management

in Semantic Web

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Web Resource/Service Integration:

Server-Based Transaction Monitor

Server Client

Server

Web

resource /

service

Web

resource /

service

Transaction Service

TM

wireless

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Web Resource/Service Integration:

Mobile Client-Base Transaction Monitor

ServerClient

Server

Web

resource /

service

TM

Web

resource /

service

wireless

wireless

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Web Resource/Service Integration:Comparison of Architectures

Server-based TM Positive:

Less wireless (sub)transactions

Rich ontological support

Smaller crash, disconnection

vulnerability

Negative: Pure customers trust

Lack of customers awareness and

control

Problematic TMs adaptation to the

customer

Client-based TM Positive:

Customers firm trust

Customers awareness and

involvement

Better TMs adaptation to the

customer

Negative: More wireless (sub)transactions

Restricted ontological support

High crash, disconnection

vulnerability

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The conceptual

scheme of the

ontology-based

transactionmanagement

with multiple e-

services

Transaction data

Service 1 ********

Service 2********

Service s ********

Services data

Transactionmonitor

Client 1

Service 1 ********

Service 2********

Service s ********

Services data

Transactionmonitor

Client r

Parameter1

Parameter2

Parametern

Recent value

Recent value

Recent value

Transaction data

Parameter1

Parameter2

Parametern

Recent value

Recent value

Recent value

Service atomic action ontologies

Parameter1

Parameter2

Parametern

Parameter ontologies

Ontologies

Name 1

Name 2

Name n

Default value / schema 1

Default value / schema 2

Default value / schema n

Name ofaction 1

inputparameters

outputparameters

Name ofaction 2

inputparameters

outputparameters

Name ofaction

inputparameters

outputparameters

Service Tree

Client1 ********

Client2********

Client r********

Clients data

Subtransactionmon itor

Service 1

Service Tree

Client1 ********

Client2********

Client r********

Clients data

Subtransactionmo nitor

Service s

Terziyan V., Ontology-Driven

Transaction Monitor for Mobile

Services, In: Proceedings of

Semweb@KR2002 Workshop on

Formal Ontology, Knowledge

Representation and Intelligent

Systems for the World Wide Web,

Toulouse, France, 19-20 April,

2002.

P C i S ti W b


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Public merchants,ublic customers, public

information providers

Clients

SMOs

SMRs

Maps

Maps

Integration,

Analysis,

Learning

Businessknowledge

Server

I

C

I

I

S

I

Negotiation,

Contracting,

Billing

Meta-

ProfilesProfiles

XML

WML

Location

Providers

Server

Map Content

Providers

Server

Content

Providers

Server

External

Environment

XML

$$$ Banks

P-Commerce in Semantic Web

Terziyan V., Architecture for Mobile P-Commerce: Multilevel Profiling

Framework, IJCAI-2001 International Workshop on "E-Business and the

Intelligent Web", Seattle, USA, 5 August 2001, 12 pp.


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A''A''

A''

1

3

2

L' '1 L' '2

A'2

A'3

A'4

A'1

L' 3

L' 2L' 1

A2

A1

A3

L 21

L 3

L 4Zero level

First level

Second level

Semantic Metanetwork for Metadata

ManagementSemantic Metanetworkis

considered formally as the

set of semantic networks,

which are put on each other

in such a way that links of

every previous semantic

network are in the same

time nodes of the next

network.

In a Semantic Metanetwork

every higher level controls

semantic structure of the

lower level.

Terziyan V., Puuronen S., Reasoning with Multilevel

Contexts in Semantic Metanetworks, In: P. Bonzon, M.

Cavalcanti, R. Nossun (Eds.), Formal Aspects in Context,

Kluwer Academic Publishers, 2000, pp. 107-126.


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Petri Metanetwork for Management Dynamics

A metapetrinetis able not only

to change the marking of apetrinet but also to reconfigure

dynamically its structure

Each level of the new

structure is an ordinary petrinet

of some traditional type.

A basic level petrinet

simulates the process of some

application.

The second level, i.e. the

metapetrinet, is used to simulate

and help controlling the

configuration change at the

basic level.

Terziyan V., Savolainen V., Metapetrinets for

Controlling Complex and Dynamic Processes,

International Journal of Information and Management

Sciences, V. 10, No. 1, March 1999, pp.13-32.

P 1

P2

P1

P4P3

t1

t2

t 3

P 3

t 2P 5

P 4

P 2

t 1

Controlling

level

Basic level

B i M k f M U i


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Bayesian Metanetwork for Management Uncertainty

2-level Bayesian Metanetwork for

modelling relevant features selection

Contextual level

Predictive level

Two-level Bayesian Metanetwork for

managing conditional dependencies

X

Y

A

B

Q

RS

X

Y

A

B

Q

RS

Two-level Bayesian Metanetwork for

managing conditional dependencies

Contextual level

Predictive level

Terziyan V., Vitko O., Bayesian Metanetworks for Mobile Web Content

Personalization, In: Proceedings of 2nd WSEAS International Conference onAutomation and Integration (ICAI02), Puerto De La Cruz, Tenerife, December 2002.


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Multidatabase Mining based on Metadata

Puuronen S., Terziyan V., Logvinovsky A., Mining Several Data Bases with

an Ensemble of Classifiers, In: T. Bench-Capon, G. Soda and M. Tjoa (Eds.),

Database and Expert Systems Applications, Lecture Notes in Computer

Science Springer Verlag V 1677 1999 pp 882 891

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Intelligent Web Application