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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
vagan@it.jyu.fi ; terziyan@yahoo.com
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
7
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
18
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
mweber@mit.jyu.fi
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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 (terziyan@yahoo.com ANDvagan@it.jyu.fi);
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
19
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.
20
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
21
Route-based personalization
Static Perspective Dynamic Perspective
M bil T i M
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Mobile Transactions Management
in Semantic Web
20
Web Resource/Service Integration:
Server-Based Transaction Monitor
Server Client
Server
Web
resource /
service
Web
resource /
service
Transaction Service
TM
wireless
21
Web Resource/Service Integration:
Mobile Client-Base Transaction Monitor
ServerClient
Server
Web
resource /
service
TM
Web
resource /
service
wireless
wireless
22
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
23
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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