Embed Size (px)
Citation preview
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]://www.cs.jyu.fi/ai/vagan/index.html
+358 14 260-4618
Vrije Universiteit Amsterdam, Fall 2002
2
Contents
Course IntroductionLectures and LinksCourse AssignmentExamples of course-related research
3
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.
4
Practical Information9 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
5
Introduction:Semantic Web - new Possibilities for
Intelligent web Applications
6
Motivation for Semantic Web
4
Web Limitations
Doubles in sizeevery six months
Average WWW searches examineonly about 25% of potentially
relevant sites and return a lot ofunwanted information
Information on web is not suitablefor software agents
World Wide Web
Semantic Web
The Semantic Web is avision: the idea of havingdata on the Web defined andlinked in a way that it can beused by machines not just fordisplay purposes, but forautomation, integration andreuse of data across variousapplications.
7
B e f o r e S e m a n t i c W e b
W e b c o n t e n t
U s e r sC r e a t o r sW W Wa n dB e y o n d
8
S e m a n tic W e b S tru c tu re
S e m a n ticA n n o ta tio n s
O n to lo g ie s L o g ic a l S u p p o rt
L a n g u a g e s T o o ls A p p lic a tio n s /S e rv ic e s
W e b c o n te n t
U se rsC re a to rsW W Wa n dB e y o n d
S e m a n ticW e b
7
Semantic Web Content: New “Users”
SemanticAnnotations
Ontologies Logical Support
Languages Tools Applications /Services
Web content
UsersCreatorsWWWandBeyond
SemanticWeb
Semantic Webcontent
UsersSemanticWeb andBeyond
Creators
applications
agents
8
Some Professions around Semantic Web
Content
Agents Annotations
Ontologies
Software engineersOntology engineers
Web designers
Content creators
Logic, Proof and Trust
AI Professionals
Mobile Computing Professionals
9
Semantic Web: Resource Integration
Shared ontology
Web resources / services / DBs / etc.
Semantic annotation
10
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
11
Word-Wide Correlated Activities
Semantic Web
Grid Computing
Web Services
Agentcities
Agentcities is a global, collaborative effort to 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 currentweb in which information is given well-definedmeaning, 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.
Distributed Artificial Intelligence inMobile Environment (2 ov.)
Lecturer: Vagan Terziyan
University of Jyvaskyla, MIT Department, Fall 2001, 2002
Vrije Universiteit Amsterdam, AI Department, Fall 2001
Intelligent Web Applications (2 ov.)
Lecturer: Vagan Terziyan
Vrije Universiteit Amsterdam, AI Department, Fall 2002
Web Content Management (6 ov.)
Lecturer: Vagan Terziyan
Jyvaskyla Polytechnic, Spring 2002
University of Jyvaskyla Experience:Examples of Related Courses
18
Digitaalisen median erityiskysymyksiä (2 ov) seminaarin aihepiiri:
Semanttinen webLecturer: Airi Salminen
University of Jyvaskyla, CS & IS Department, Spring 200218
Structured Electronic Documentation
Lecturer: Matthieu Weber
University of Jyvaskyla, MIT Department, Fall 2001, 2002
Intelligent Information Integrationin Mobile Environment (4 ov.)
Lecturer: Vagan TerziyanUniversity of Jyvaskyla, MIT Department, Spring 2002
13
IWA Course (Part 1): Lectures
14
Lecture 1: Web Content Personalization Overview
1
Web Content PersonalizationOverview
Based on the Tutorials of K. Garvie Brown,R. Wilson, M. Shamos and others3
Personalizing Web Resources for a User -one of the basic abilities of an intelligent agent
WebResource
Users
http://www.cs.jyu.fi/ai/vagan/Personalization.ppt
15
Lecture 2: Collaborative Filtering
Collaborative Filtering
Partially based on tutorials and approachesof GroupLens, Mginetechnologies andWeb Museum research groups
Improving Personalized Service based onFeedback from Users (Collaborative Filtering)- one of the basic abilities of an intelligent agent
WebResource
Users
1. Recommendation
2. Feedback
3. Betterrecommendations
...
http://www.cs.jyu.fi/ai/vagan/Collaborative_Filtering.ppt
16
Lecture 3: Dynamic Integration of Virtual Predictors
2
Discovering Knowledge from Data - one ofthe basic abilities of an intelligent agent
Data Knowledge
Dynamic Integration ofVirtual Predictors
Vagan TerziyanUniversity of Jyvaskyla, Finland
e-mail: [email protected]://www.cs.jyu.fi/ai/vagan/index.html
http://www.cs.jyu.fi/ai/vagan/Virtual_Predictors.ppt
17
Lecture 4: Introduction to Bayesian Networks
2
Discovering Casual Relationship from the Dynamic
Environmental Data and Managing Uncertainty - areamong the basic abilities of an intelligent agent
Casual networkwith Uncertainty
DynamicEnvironment
beliefs
Introductionto Bayesian Networks
Based on the Tutorials and Presentations:Based on the Tutorials and Presentations:(1) Dennis M.(1) Dennis M. Buede Buede Joseph A. Joseph A. Tatman Tatman, Terry A. , Terry A. BresnickBresnick;;(2) Jack(2) Jack Breese Breese and Daphne and Daphne KollerKoller;;(3) Scott Davies and Andrew Moore;(3) Scott Davies and Andrew Moore;(4) Thomas Richardson(4) Thomas Richardson(5) (5) Roldano CattoniRoldano Cattoni(6) (6) Irina Irina RichRich
http://www.cs.jyu.fi/ai/vagan/Bayes_Nets.ppt
18
Lecture 5: Web Mining
2
Discovering Knowledge from and about WWW -is one of the basic abilities of an intelligent agent
Knowledge
WWW
Web Mining
Based on tutorials and presentations:J. Han, D. Jing, W. Yan, Z. Xuan, M. Morzy, M. Chen, M. Brobbey, N. Somasetty, N. Niu,
P. Sundaram, S. Sajja, S. Thota, H. Ahonen-Myka, R. Cooley, B. Mobasher, J. Srivastava
http://www.cs.jyu.fi/ai/vagan/Web_Mining.ppt
19
Lecture 6: Multidatabase Mining
Discovering Knowledge from Distributed andHeterogeneous Databases - is one of the basicabilities of an intelligent agent
Knowledge
Distributed andheterogeneousdatabases
M u l t i d a t a b a s e M i n i n g
B a s e d o n t u t o r i a l s a n d p r e s e n t a t i o n s :
J . H a n , C . I s i k , M . K a m b e r , A . L o g v i n o v s k i y , S . P u u r o n e n , V . T e r z i y a n
D B 1
?x
C l a s s i f i e r m
C l a s s i f i e r 1
D B n
http://www.cs.jyu.fi/ai/vagan/MDB_Mining.ppt
20
Lecture 7: Metamodels for Managing Knowledge
2
Creating and Managing Knowledge According toDifferent Levels of Possible Context - are amongthe basic abilities of an intelligent agent
DataKnowledge
Contexts
Metacontexts
Metaknowledge
Meta-metaknowledge
1
Metamodels for ManagingKnowledge
Vagan Terziyan
University of Jyvaskyla, Finlande-mail: [email protected]
http://www.cs.jyu.fi/ai/vagan/index.html
http://www.cs.jyu.fi/ai/vagan/Metamodels.ppt
21
Lecture 8: Knowledge Management
Making Personal Knowledge Available to Others andDealing with Knowledge Taken from Multiple Sources- are among the basic abilities of an Intelligent Agent
K n o w l e d g e K n o w l e d g e M a n a g e m e n tM a n a g e m e n t
B a s e d o n t u t o r ia ls a n d p r e s e n t a t io n s : R . B e r g m a n n , M . M . R ic h t e r , D . J . S k y r m e ,B e l la n e t I n t ’ l , S U R F - A S , R . L . H e r t in g , R . S m i t h , F . J . K u r f e s s , R . D ie n g a t a l . , M .S in t e k , A . A b e c k e r , A . B e r n a r d i , D . K a r a g ia n n is , R . T e le s k o , L . K e r s c h b e r g
“ G iv e a m a n a f i s h - f e e d h im f o r a d a y ;t e a c h h im h o w t o f i s h - f e e d h im f o r a l i f e t im e ”C h in e s e p r o v e r b
http://www.cs.jyu.fi/ai/vagan/Knowledge_Management.ppt
22
Lecture 9: E-Services in Semantic Web
E-Services in Semantic Web
Vagan Terziyan
MIT Department, University of J yvaskyla // AI Department, Kharkov National University of Radioelectronics
[email protected]://www.cs.jyu.fi/ai/vagan
+358 14 260-2347
Managing Transactions with Distributed E-Services
and providing Integrated Service to a User - areamong the basic abilities of an Intelligent Agent
http://www.cs.jyu.fi/ai/vagan/E-Services.ppt
23
IWA Course (Part 1): Practical Assignment
24
Practical assignment in briefStudents 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.
25
Evaluation criteria for practical assignment
Content and Completeness;Clearness and Simplicity;Discovered Connections to IWA Course Material;Originality, Personal Conclusions and Critics;Design Quality.
26
Format, Submission and DeadlinesFormat: 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.
27
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
28
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
29
University of Jyvaskyla Experience: Examples of Course-Related Research
30
18
Multimeetmobile Project (2000-2001)
Information TechnologyResearch Institute(University of Jyvaskyla):Customer-oriented research anddevelopment in Information Technology
http://www.titu.jyu.fi/eindex.html
Multimeetmobile (MMM) Project(2000-2001):Location-Based Service System and TransactionManagement in Mobile Electronic Commerce
http://www.cs.jyu.fi/~mmm
Academy of FinlandProject (1999):Dynamic Integration ofClassification Algorithms
Mobile Location-Based Service in Semantic Web
19
M-Commerce LBS systemhttp://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-basedService system (MLS), has been developed.MLS is a general LBS system for mobileusers, offering map and navigation acrossmultiple geographically distributed servicesaccompanied with access to location-basedinformation through the map on terminal’sscreen. MLS is based on Java, XML and usesdynamic selection of services for customersbased on their profile and location.
Virrantaus K., Veijalainen J., Markkula J.,Katasonov A., Garmash A., Tirri H., Terziyan V.,Developing GIS-Supported Location-BasedServices, In: Proceedings of WGIS 2001 - FirstInternational Workshop on Web GeographicalInformation Systems, 3-6 December, 2001, Kyoto,Japan, pp. 423-432.
2 0
A d a p t i v e i n t e r f a c e f o r M L S c l i e n t
O n l y p r e d i c t e d s e r v i c e s , f o r t h e c u s t o m e r w i t h k n o w n p r o f i l ea n d l o c a t i o n , w i l l b e d e l i v e r e d f r o m M L S a n d d i s p l a y e d a tt h e m o b i l e t e r m i n a l s c r e e n a s c l i c k a b l e “ p o i n t s o f i n t e r e s t ”
21
Route-based personalization
Static Perspective Dynamic Perspective 2 2
I n d u c t i v e l e a r n i n g o f c u s t o m e rp r e f e r e n c e s w i t h i n t e g r a t i o n o f p r e d i c t o r s
rrmrr yxxx ,...,, 21
S a m p l e I n s t a n c e s
tmtt xxx ,...,, 21
y t
L e a r n i n g E n v i r o n m e n t
P 1 P 2 . . . P n
P r e d i c t o r s / C l a s s i f i e r s
T e r z i y a n V . , D y n a m i c I n t e g r a t i o n o f V i r t u a l P r e d i c t o r s , I n : L . I . K u n c h e v a , F .S t e i m a n n , C . H a e f k e , M . A l a d j e m , V . N o v a k ( E d s ) , P r o c e e d i n g s o f t h e I n t e r n a t i o n a l I C S CC o n g r e s s o n C o m p u t a t i o n a l I n t e l l i g e n c e : M e t h o d s a n d A p p l i c a t i o n s - C I M A ' 2 0 0 1 , B a n g o r ,W a l e s , U K , J u n e 1 9 - 2 2 , 2 0 0 1 , I C S C A c a d e m i c P r e s s , C a n a d a / T h e N e t h e r l a n d s , p p . 4 6 3 - 4 6 9 .
31
Mobile Transactions Management in Semantic Web
20
Web Resource/Service Integration:Server-Based Transaction Monitor
Server Client
Server
Webresource /
service
Webresource /
service
Transaction Service
TM
wireless
21
Web Resource/Service Integration:Mobile Client-Base Transaction Monitor
ServerClient
Server
Webresource /
service
TM
Webresource /
service
wireless
wireless
22
Web Resource/Service Integration:Comparison of Architectures
Server-based TM Positive:
Less wireless (sub)transactions
Rich ontological support
Smaller crash, disconnectionvulnerability
Negative: Pure customer’s trust
Lack of customer’s awareness andcontrol
Problematic TM’s adaptation to thecustomer
Client-based TM Positive:
Customer’s firm trust
Customer’s awareness andinvolvement
Better TM’s adaptation to thecustomer
Negative: More wireless (sub)transactions
Restricted ontological support
High crash, disconnectionvulnerability
2 3
T h e c o n c e p t u a ls c h e m e o f t h eo n t o l o g y - b a s e dt r a n s a c t i o nm a n a g e m e n tw i t h m u l t i p l e e -s e r v i c e s
T r a n s a c t i o n d a t a
S e r v i c e 1 * * * * * * * *
S e r v i c e 2 * * * * * * * *
…
S e r v i c e s * * * * * * * *
S e r v i c e s d a t a
T r a n s a c t i o n m o n i t o r
C l i e n t 1
…
S e r v i c e 1 * * * * * * * *
S e r v i c e 2 * * * * * * * *
…
S e r v i c e s * * * * * * * *
S e r v i c e s d a t a
T r a n s a c t i o n m o n i t o r
C l i e n t r
P a r a m e t e r 1
P a r a m e t e r 2
…
P a r a m e t e r n
R e c e n t v a l u e
R e c e n t v a l u e
…
R e c e n t v a l u e
T r a n s a c t i o n d a t a
P a r a m e t e r 1
P a r a m e t e r 2
…
P a r a m e t e r n
R e c e n t v a l u e
R e c e n t v a l u e
…
R e c e n t v a l u e
S e r v i c e a t o m i c a c t i o n o n t o l o g i e s
P a r a m e t e r 1
P a r a m e t e r 2
…
P a r a m e t e r n
P a r a m e t e r o n t o l o g i e s
O n t o l o g i e s
N a m e 1
N a m e 2
…
N a m e n
D e f a u l t v a l u e / s c h e m a 1
D e f a u l t v a l u e / s c h e m a 2
…
D e f a u l t v a l u e / s c h e m a n
N a m e o f a c t i o n 1
i n p u t p a r a m e t e r s
o u t p u t p a r a m e t e r s
N a m e o f a c t i o n 2
i n p u t p a r a m e t e r s
o u t p u t p a r a m e t e r s
N a m e o f a c t i o n k
i n p u t p a r a m e t e r s
o u t p u t p a r a m e t e r s
…
S e r v i c e T r e e
C l i e n t 1 * * * * * * * *
C l i e n t 2 * * * * * * * *
…
C l i e n t r * * * * * * * *
C l i e n t s d a t a
S u b t r a n s a c t i o n m o n i t o r
S e r v i c e 1
S e r v i c e T r e e
C l i e n t 1 * * * * * * * *
C l i e n t 2 * * * * * * * *
…
C l i e n t r * * * * * * * *
C l i e n t s d a t a
S u b t r a n s a c t i o n m o n i t o r
S e r v i c e s
…
T e r z i y a n V . , O n t o l o g y - D r i v e nT r a n s a c t i o n M o n i t o r f o r M o b i l eS e r v i c e s , I n : P r o c e e d i n g s o fS e m w e b @ K R 2 0 0 2 W o r k s h o p o nF o r m a l O n t o l o g y , K n o w l e d g eR e p r e s e n t a t i o n a n d I n t e l l i g e n tS y s t e m s f o r t h e W o r l d W i d e W e b ,T o u l o u s e , F r a n c e , 1 9 - 2 0 A p r i l ,2 0 0 2 .
32
Public merchants,public customers, publicinformation providers
…
…
Clients
SMOs
SMRs
Maps<path network>
Maps<business points>
Integration,Analysis,Learning
Businessknowledge
Server
I
C
I
I
S
I
Negotiation,Contracting,
Billing
Meta-Profiles
Profiles
XMLWML
LocationProviders
Server
Map ContentProviders
Server
ContentProviders
Server
…
…
…
ExternalEnvironment
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.
33
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
L2L
1
L3
L4
Zero level
First level
Second level
Semantic Metanetwork for Metadata Management
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.
34
Petri Metanetwork for Management Dynamics
•A metapetrinet is able not only to change the marking of a petrinet 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
Controllinglevel
Basic level
35
Bayesian Metanetwork for Management Uncertainty
3-level Bayesian Metanetwork forManaging Feature Relevance
X
Y
A
BQ
RSX
Y
A
B
Q
RS
2 -lev e l B ay esian M etan e tw o rk fo rm o d e llin g re lev an t fea tu res’ se lec tio n
C o n te x tu a l le ve l
P re d ic tiv e le v e l
Two-level Bayesian Metanetwork formanaging conditional dependencies
X
Y
A
BQ
RS
X
Y
A
B
Q
RS
T w o -lev e l B ay esian M etan e tw o rk fo rm an ag in g co n d itio n a l d ep en d en c ies
C o n te x tu a l le ve l
P re d ic tiv e le v e l
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.
36
Multidatabase Mining based on Metadata
MANY:MANY
DB 1
Classifier m
Classifier 1
DB n
ONE:MANY
Classifier m
Classifier 1
DB
MANY:ONE
DB 1
Classifier
DB n
ONE:ONE
DB
Classifier
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.
