Intelligent Web Applications

Report
Intelligent Web
Applications (Part 1)
Course Introduction
Vrije Universiteit Amsterdam, Fall 2002
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
Contents




Course Introduction
Lectures and Links
Course Assignment
Examples of course-related
research
2
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 shared
web 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.
3
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 lecturer until 10 December);
 Exam - there will be no exam. Evaluation mark
for this part of the course will be given based on
the Practical Assignment
4
Introduction:
Semantic Web - new Possibilities for
Intelligent web Applications
5
Motivation for Semantic Web
Semantic Web Structure
Before Semantic Web
Semantic
Annotations
Ontologies
Logical Support
Languages
Tools
Applications /
Services
Semantic
Web
WWW
and
Beyond
Creators
Users
WWW
and
Beyond
Web content
7
Creators
Users
Web content
6
8
Semantic Web Content: New “Users”
Semantic
Web and
Beyond
Users
Creators
applications
Semantic Web
content
agents
Semantic
Annotations
Ontologies
Logical Support
Languages
Tools
Applications /
Services
Semantic
Web
WWW
and
Beyond
Creators
Users
Web content
7
Some Professions around Semantic Web
AI Professionals
Content creators
Content
Mobile Computing
Professionals
Ontologies
Agents
Logic, Proof
and Trust
Web designers
Annotations
Ontology engineers
Software engineers
8
Semantic Web: Resource Integration
Semantic
annotation
Shared
ontology
Web resources /
services / DBs / etc.
9
What else Can be Annotated
for Semantic Web ?
External world
resources
Web resources /
services / DBs / etc.
Web users
(profiles,
preferences)
Shared
ontology
Web agents /
applications
Web access
devices
10
Word-Wide Correlated Activities
Semantic Web
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
Agentcities is a global, collaborative effort
to construct an open network of on-line systems
hosting diverse agent based services.
Agentcities
Grid Computing
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.
Web 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.
11
University of Jyvaskyla Experience:
Examples of Related Courses
Digitaalisen median erityiskysymyksiä (2 ov)
seminaarin aihepiiri:
Semanttinen web
Structured Electronic Documentation
Lecturer: Matthieu Weber
Lecturer: Airi Salminen
University of Jyvaskyla, CS & IS Department, Spring 2002
18
University of Jyvaskyla, MIT Department, Fall 2001, 2002
[email protected]
18
IWA Course (Part 1): Lectures
13
Lecture 1: Web Content Personalization Overview
http://www.cs.jyu.fi/ai/vagan/Personalization.ppt
14
Lecture 2: Collaborative Filtering
http://www.cs.jyu.fi/ai/vagan/Collaborative_Filtering.ppt
15
Lecture 3: Dynamic Integration of Virtual Predictors
http://www.cs.jyu.fi/ai/vagan/Virtual_Predictors.ppt
16
Lecture 4: Introduction to Bayesian Networks
http://www.cs.jyu.fi/ai/vagan/Bayes_Nets.ppt
17
Lecture 5: Web Mining
http://www.cs.jyu.fi/ai/vagan/Web_Mining.ppt
18
Lecture 6: Multidatabase Mining
http://www.cs.jyu.fi/ai/vagan/MDB_Mining.ppt
19
Lecture 7: Metamodels for Managing Knowledge
http://www.cs.jyu.fi/ai/vagan/Metamodels.ppt
20
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
21
Lecture 9: E-Services in Semantic Web
http://www.cs.jyu.fi/ai/vagan/E-Services.ppt
22
IWA Course (Part 1): Practical
Assignment
23
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/her
choice 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.
24
Evaluation criteria for practical
assignment
 Content and Completeness;
 Clearness and Simplicity;
 Discovered Connections to IWA Course
Material;
 Originality, Personal Conclusions and Critics;
 Design Quality.
25
Format, Submission and Deadlines
 Format: PowerPoint ppt. (winzip encoding
allowed), name of file is student’s 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] AND
[email protected]);
 Notification of evaluation - until 15 December.
26
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
27
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
28
University of Jyvaskyla Experience:
Examples of Course-Related Research
29
Mobile Location-Based Service
in Semantic Web
M-Commerce LBS system
Adaptive interface for MLS client
http://www.cs.jyu.fi/~mmm
In the framework of the Multi Meet Mobile
(MMM) project at the University of Jyväskylä,
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 terminal’s
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.
19
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” 20
Route-based personalization
30
Static Perspective
Dynamic Perspective
21
Mobile Transactions Management
in Semantic Web
Web Resource/Service Integration:
Web Resource/Service Integration:
Server-Based Transaction Monitor
Mobile Client-Base Transaction Monitor
Web
resource /
service
Web
resource /
service
Server
Client
TM
wireless
Client
Server
wireless
wireless
TM
Web
resource /
service
Web
resource /
service
Transaction Service
Server
The conceptual
scheme of the
ontology-based
transaction
management
with multiple eservices
Web Resource/Service Integration:
Comparison of Architectures
 Server-based TM




Less wireless (sub)transactions
Rich ontological support
Smaller crash, disconnection
vulnerability




 Client-based TM
Positive:




Negative:
Pure customer’s trust
Lack of customer’s awareness and
control
Problematic TM’s adaptation to the
customer
Positive:
Customer’s firm trust
Customer’s awareness and
involvement
Better TM’s adaptation to the
customer




Server
20
Client 1
Transaction data
Client r
Services data
Transaction data
Negative:
More wireless (sub)transactions
Restricted ontological support
High crash, disconnection
vulnerability
22
Services data
Parameter 1 Recent value
Service 1 ********
Parameter 1 Recent value
Service 1 ********
Parameter 2 Recent value
Service 2 ********
Parameter 2 Recent value
Service 2 ********
…
…
…
Parameter n Recent value
…
Service s ********
Transaction monitor
…
…
Parameter n Recent value
…
Service s ********
Transaction monitor
Ontologies
Service atomic action ontologies
Parameter ontologies
Parameter 1
Name 1
Default value / schema 1
Parameter 2
Name 2
Default value / schema 2
…
…
…
Parameter n
Name n
Default value / schema n
input parameters
input parameters
Name of action 1
Name of action 2
Service 1
output parameters
output parameters
Service s
Subtransaction monitor
Service Tree
input parameters
Name of action k
…
output parameters
Terziyan V., Ontology-Driven
Transaction Monitor for Mobile
Services, In: Proceedings of
[email protected] Workshop on
Formal Ontology, Knowledge
Representation and Intelligent
Systems for the World Wide Web,
Toulouse, France, 19-20 April,
2002.
21
Subtransaction monitor
Clients data
Service Tree
Clients data
Client 1 ********
Client 1 ********
Client 2 ********
Client 2 ********
…
Client r ********
…
…
Client r ********
23
31
P-Commerce in Semantic Web
Clients
Public merchants,
public customers, public
information providers
…
External
Environment
Server
Maps
Maps
<path network>
<business points>
SMOs
I
I
C
S
I
MetaProfiles
XML
WML
SMRs
…
Map Content
Providers
Server
Integration,
Analysis,
Learning
Location
Providers
Server
I
Business
knowledge
Profiles
…
Negotiation,
Contracting,
Billing
XML
…
Content
Providers
Server
…
$ $ $ Banks
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.
32
Semantic Metanetwork for Metadata
Management
A' '
2
L ''
L ''
1
S e cond le v e l
2
A' '
3
A' '
1
A'
L'
A'
A'
1
L'
3
A'
2
1
L'
2
L 1
A
2
4
Firs t le v e l
3
L 2
L 4
Z e ro le v e l
A
1
L 3
A
3
Semantic Metanetwork is
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.
33
Petri Metanetwork for Management Dynamics
P´3
P´2
t´1
P´1
t´3
P´4
Controlling
level
P´5
t´2
t1
Basic level
P1
P2
• Each level of the new
structure is an ordinary petrinet
of some traditional type.
• A basic level petrinet
simulates the process of some
application.
P4
P3
•A metapetrinet is able not only
to change the marking of a
petrinet but also to reconfigure
dynamically its structure
t2
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.
• The second level, i.e. the
metapetrinet, is used to simulate
and help controlling the
configuration change at the
basic level.
34
Bayesian Metanetwork for Management Uncertainty
Two-level Bayesian Metanetwork for
managing conditional dependencies
Two-level Bayesian Metanetwork for
managing conditional dependencies
Contextual level
A
X
Q
B
Y
X
A
Predictive level
S
R
Q
B
S
Y
R
2-level Bayesian Metanetwork for
modelling relevant features’ selection
Contextual level
Predictive level
Terziyan V., Vitko O., Bayesian Metanetworks for Mobile Web Content
Personalization, In: Proceedings of 2nd WSEAS International Conference on
Automation and Integration (ICAI’02), Puerto De La Cruz, Tenerife, December 2002.
35
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.
36

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