Web Intelligence Meets BrainInformatics an impending revolution in WI and Brain Sciences

1
Web Intelligence Meets Brain-Informatics- an
impending revolution in WI and Brain Sciences -
Ning Zhong
Web Intelligence Consortium Maebashi Institute
of Technology, Japan
2
Acknowledgements

• Prof. Piotr Szczepaniak, organizers of AWIC 2005
• Jiming Liu, Yiyu Yao, Jinglong Wu, Edward
  Feigenbaum, John McCarthy, Tom Mitchell, Setsuo
  Ohsuga, Benjamin Wah, Philip Yu, Lotfi
  Zadeh, Xindong Wu etc.
• WIC Technical Committee and WIC Research Centers
  in Australia, Beijing, Canada, Hong Kong, India,
  Japan, Korea, Mexico, Poland, Spain, France, and
  UK, among others
• Students and Post-doc/Visitors at
• Maebashi Institute of Technology
• Beijing University of Technology

3
AIIT Web Intelligence (WI)?

• WI explores the fundamental roles as well as
  practical impacts of
• Artificial Intelligence (AI) (e.g., knowledge
  representation, planning, knowledge discovery,
  agents, and social intelligence) and
• Advanced Information Technology (IT) (e.g.,
  wireless networks, ubiquitous devices, social
  networks, and data/knowledge grids)
• on the next generation of Web-empowered
  systems, services, environments, and activities.

Zhong, N , Liu, J , and Yao, Y Y (eds.) Web
Intelligence, Springer, 2003
4
Perspectives of WI (Yao-Zhong-Liu,
2001)

• WI may be viewed as applying results from
  existing disciplines (IT and AI) to a totally new
  domain.
• WI introduces new problems and challenges to the
  established disciplines.
• WI may be viewed as an enhancement or an
  extension of IT and AI.

5
A New Perspective of WI WI Meets
Brain-Informatics (BI)

• New instrumentation (fMRI etc) and advanced IT
  are causing an impending revolution in WI and
  Brain Sciences (BS).
• The WI based portal techniques will provide a
  new powerful platform for BS.
• The new understanding and discovery of human
  intelligence models in BS will yield a
  new generation of WI research/development.

6
A New Perspective of WI

• New instrumentation (fMRI etc) and advanced IT
  are causing an impending revolution in WI and
  Brain Sciences (BS).
• The WI based portal techniques will provide a
  new powerful platform for BS.
• The new understanding and discovery of human
  intelligence models in BS will yield a
  new generation of WI research/development.

7
What Are Web-Based Portals?

• Enabling an organization (company, community)
  to create a virtual organization on the Web where
  key production/information steps are outsourced
  to partners and customers.
• A single gateway to personalized information
  needed to enable informed interdisciplinary
  research, services, and/or business activities.
• One of the most sophisticated applications in
  WI.

8
Physical World
Virtual World
Knowledge

• Applications
• e/m-Business
• e/m-Science
• e/m-Learning
• e/m-Government

• Intelligent INFORMATION Analysis

Internet
Web-Based Information
Web Accessible Information
Intelligent DATA Pre-processing

• Ubiquitous Agents
• Adaptive Interface
• Personal Assistants
• Distributed Cooperative Work

Heterogeneous Information Sources
Intelligent Resource Intermediary Coordination
9
What Is Brain-Informatics (BI)?

• Brain Informatics (BI) is a new interdisciplinary
  field to study human information processing
  mechanism systematically from both macro and
  micro points of view by cooperatively using
• -- experimental brain/cognitive technology
  and
• -- WI centric advanced information
  technology.
• In particular, it attempts to understand human
  intelligence in depth, towards a holistic view at
  a long-term, global field of vision to understand
  the principle, models and mechanisms of human
  multi-perception, language, memory, reasoning and
  inference, problem-solving, learning, discovery
  and creativity.

10
Brain Sciences v.s. Brain Informatics

• Although brain sciences have been studied from
  different disciplines such as cognitive science
  and neuroscience, Brain Informatics (BI)
  represents a potentially revolutionary shift in
  the way that research is undertaken.
• It attempts to capture new forms of collaborative
  and interdisciplinary work.
• In this vision, new kinds of BI methods and
  global research communities will emerge, through
  infrastructure on the Wisdom Web and Knowledge
  Grids that enables high-speed and distributed,
  large-scale analysis and computations, and
  radically new ways of sharing data/knowledge
  repositories.

11
WI centric IT
Brain Informatics
Cognitive Science
Neuroscience
12
Our Recent Works (Zhong-Wu, 01-04)

• Investigating human multi-perception mechanism by
  cooperatively using cognitive technology and data
  mining techniques for developing artificial
  systems which match human ability in specific
  aspects.
• Building a multi-database mining system on
  the Wisdom Web and Grid platform for developing a
  brain-informatics portal.

13
Main Questions

• How to design the psychological and physiological
  experiments for obtaining various data from human
  multi-perception mechanism?
• How to analyze such data from multiple aspects
  for discovering new models of human
  multi-perception?

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physiological experiments
traditional psychometrics
(fMRI, EEG, )
results (discrete)
results (image)
results (wave)
data modeling and transformation
image data
wave data
discrete data
data mining knowledge discovery
models of human multi-perception
test of the multi-perception models
application of the models
15
Data from Physiometry
fMRI experimental apparatus
image of the electroencephalograph (EEG)
The future of cognitive science and neuroscience
will be affected by the ability to do large-scale
analysis of fMRI brain activations.
fMRI functional Magnetic Resonance Imaging
16
Design of Experiments

• Human subjects perform simple arithmetic

stimulus
task
17
Time Chart
start
end
on-task
off-task
no-task
no-task
5060
180
1015
180
(sec)

(sec)
(sec)
35
18
fMRI Images

• Understanding human multi-perception mechanism
  by analyzing fMRI images obtained from human
  visual and auditory psychological experiments

19
Visual and Auditory Calculation
The results of the visual and auditory
calculation processing in fMRI experiments
20
Peculiarity Oriented Analysis

• We observe that fMRI/EEG brain activations are
  peculiar ones with respect to the specific state
  and the related part activated by a stimulus.
• We proposed a way of peculiarity oriented mining
  (POM) in multiple human brain data.
• Peculiarity is a kind of interestingness.
  Peculiarity relationships/rules (with common
  sense) may be hidden in a relatively small number
  of data.

21
Peculiarity Oriented Mining (1) (Zhong et al
99-04)

• The main task of peculiarity oriented mining
  (POM) is the identification of peculiar data.
• Peculiarity Factor (PF) evaluates whether
• occurs in relatively small number and is
  very different from other data

22
Peculiarity Oriented Mining (2)
23
Peculiarity Oriented Mining (3)

• After the PF evaluation, the peculiar data are
  selected by using a threshold value
• where can be specified by a user. That is, if
  is over the threshold value,
  
• is a peculiar data.

24
fMRI Data Is Very Large

• The number of the data for each subject is
  122,880 (i.e. 64pixelx64pixelx30images)
• 9 subjects x 122,880 1,105,920
• A way for data reduction is to use a tool called
  Talairach Daemon that is based on the Brodman
  human brain map as prior knowledge.

25
Brodmann Brain Map (Prior Knowledge)
(a general view of brain functions)
BA
Function
visual information processing auditory
information processing motor area (language
representation) Wernickes area (language
understanding,calculation) Brocas area (language
representation) working memory
17,18,19 22,41,42 6 39,40 (21,22,37) 44,45,47 9,
45,46
26
POM Based fMRI Data Mining Processes
formalize/transform fMRI data by MEDx/SPM
obtain BA values based on the TD coordinate
obtain BA values based on the TD coordinate
carry out POM only on the obtained BA values
peculiar data obtained
peculiar data obtained
Data Mining (1)
27
A Multi-Step Process

• Various tools (e.g. MEDx/SPM, TD) can be
  cooperatively used with POM in a multi-step
  process for pre-processing (data cleaning,
  modeling and transformation), mining and
  post-processing.
• Goal Machine-processing/understanding to replace
  human-expert centric visualization.

28
Part of Results (PF Values) Based on BA (Visual)
29
Part of Results (PF Values) Based on BA (Auditory)
30
An Interesting Hypothesis
By comparing the results of mining in auditory
and visual calculation, we can confirm an
interesting hypothesis of human information
processing
Auditory information may be transferred into
visual information, in some cases of advanced
information processing such as calculation.
A possible explanation for the hypothesis is that
there may be some common areas of auditory and
visual information mechanisms.
31
Observations

• Although many cognitive/brain scientists have
  already studied human information processing
  mechanism of visual and auditory, separately, the
  relevance between visual and auditory information
  processing needs to be investigated in depth.
• We need to extend multi-data source by including
  EEG brain waves for multi-aspect analysis in
  various data mining approaches.

32
Brain Waves (EEG)
Sample Brain Waves
Visualization in BIMUTAS

• Cooperative use with fMRI images
  for multi-aspect analysis

33
Comparison Between the Space and Time Resolution
of Various Brain Measurements

• Time Resolution
• 1000s PET
  
• 100s shape measurement
• blood-flow measurement
• 10s electro-magnetic measurement
  
• 
  fMRI OT
• 1s unobtrusive
• 
  MRI
• 100ms
• obtrusive
  CT
• 10m
• MEM OR
  MEG EEG
• 1ms
• 0.01 0.1 1
  10 100
• Space
  Resolution mm

fMRI has excellent spatial resolution, EEG has
excellent temporal resolution
34
Multiple Data Sources

• Each method (fMRI and EEG) has its own good
  points and weakness from the aspects of space and
  time resolution.
• fMRI provides images of functional brain activity
  to observe dynamic activity patterns within
  different parts of the brain for a given task. It
  is excellent in space resolution, but inferior
  time resolution.
• EEG provides information about the electrical
  fluctuations between neurons that also
  characterize brain activity, and measurements of
  brain activity at resolutions approaching real
  time.
• Multiple data sources from various practical
  measuring methods are required.

35
Position of Electrode
Front

• The number of effective channels 24
• Extension of the international 10-20 system
• Sampling frequency is 500Hz

36
A POM Based EEG Data Mining Process(Motomura-Zhon
g, 04-05)

Noise Filtering (FIR Filter)
Format Transformation
Data Extraction
Experiments
Special Format ? Text Data
Check experiment
Model Transformation
POM
Data Consolidation
Evaluating the Mined Results
The Discovered Knowledge
37
Preprocessing

• ?The FIR band pass filter (460Hz, 512tap) is
  used to remove noises.
• ?They are clustered for each task after noise
  removal.
• ?Data is extracted in consideration of eye
  movement.

Invalidation
Available
Blue Beep signal Red Eye movement Black
Brain waves
Blinking
Beep sound
38
Model Transformation

• A problem of using the POM in time series data
  is that it is influenced by a phase.
• That is, it is necessary to shift the cut area
  of each data so that correlation may become
  strong.

This mining method needs the model transformation
39
2-variate Histogram

• Transforming the filtered brain waves into
  2-variate histogram with the slope and the
  potential

Class of the potential (-23.523.5, l45)
Class of the slope (-4.74.7 m47)
Frequency of appearance
40

• 2-variate Histogram (2)

Frequency of appearance is denoted by different
colors or numerical values in cells
Potential mV
62
5862
26
-22
26
-62-58
-62
Slope mV/2msec
11.7
-11.7
0.30.9
-0.30.3
-0.91.5
11.111.7
-11.7-11.1
41
Example of 2-variate Histogram
Subject B Position of Electrode
(CH) CZ
42
Data Preparation for Findingthe Peculiar Channel

• 1. Forming the data for peculiarity analysis by
  calculating the difference between on-task and
  no-task in each channel, respectively.
• Example

?
on-task
no-task
the data to be used for peculiarity analysis
2. The transformed data are used for POM.
43
Finding the Peculiar Channel
44
Explanatory Notes
45
Experimental Results (1)
46
Multi-Aspect Analysis(Zhong-Ohshima, 03-04)

• Using various data mining techniques
  (association, peculiarity-oriented,
  classification, etc) for analyzing in
  multiple data sources.
• Studying the neural structures of the activated
  areas and try to understand
• - how a peculiar part of the brain
  operates
• - how they work cooperatively to implement
  
• a whole information processing
• - how they are linked functionally to
  individual
• differences in performance.
• Changing the perspective of cognitive
  scientists from a single type of experimental
  data towards a holistic view at a
  long-term, global field of vision.

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A Brain-Informatics
Portal Architecture
ontologies
ontologies
ontologies
KDD Services
Data-grid
Data-grid
Data-flow Manager
Mining-flow Manager
Mining-grid
Mining-grid
Knowledge-flow Manager
Knowledge-grid
Knowledge-grid
Grid-Based Middleware
Browsing/Sampling Data KDD Process Planning Model
Analysis/Refinement Visualization
Wisdom Web-2
Private Workspac-1
Private Workspace-2
Coordinating Agent Cooperative
planning Distributing KDD tasks Combining partial
models
Local DB
Local DB
Shared Workspace
KDD process plan
KDD process plan
48
Ontologies Based Description/Integration for
Multi-Data Source/DM Agents

• Providing a formal, explicit specification for
  integrated
• use of multiple data sources in a semantic
  way.
• Providing conceptual representation about the
  sorts and
• properties of data/knowledge and DM agents,
  as well as relations between data/knowledge and
  DM agents.
• Providing a vocabulary of terms and relations to
  model the domain, and specifying how you view
  the data sources and how to use DM agents.
• Providing a common understanding of multiple data
  sources that can be communicated between
  grid-based DM agents.

49
An Ontology of Data Mining Agents
Preprocess
Post-process
...
...
...
MVF
...
Cluster
...
50
The Wisdom Web for Brain-informatics
(Liu-Zhong-Yao, 02-03)

• To provide
• - not only a medium for information/knowledge
  exchange/sharing
• - but also a type of man-made resources for
  sustainable knowledge creation and scientific
  evolution.
• The Wisdom Web will reply on grid-like agencies
• - self-organize, learn, and evolve their courses
  of actions in order to perform service tasks as
  well as their identities and interrelationships
  in communities
• - cooperate and compete among themselves in order
  to optimize their as well as others resources
  and utilities.

51
WI Technologies for Intelligent Portals
Ubiquitous Computing
Multi-modal Interaction
Web Information Retrieval
i-Portals
Web Mining and Farming
Web Agents
Knowledge Networks and Management
Social Networks
Grid Computing
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WI for i-Portals

Web Intelligence
53
Four Levels of WI Support
Application-level ubiquitous computing and social
intelligence utilities
Level-4
Knowledge-level information processing and
management tools
Level-3
Interface-level multi-media presentation standards
Level-2
support
Internet-level communication, infrastructure,
and security protocols
Level-1
54
A New Perspective of WI

• New instrumentation (fMRI etc) and advanced IT
  are causing an impending revolution in WI and
  Brain Sciences (BS).
• The WI based portal techniques will provide a
  new powerful platform for BS.
• The new understanding and discovery of human
  intelligence models in BS will yield a
  new generation of WI research/development.

55
Towards Human-Level WI

• One of fundamental goals of WI research is
  to understand and develop Wisdom Web based
  intelligent systems that integrate all the
  human-level capabilities such as real-time
  response, robustness, autonomous interaction with
  their environment, communication with natural
  language, commonsense reasoning, planning,
  learning, discovery and creativity.

56
Web Intelligence (WI)
Understanding intelligence in depth
Combining the three intelligence related areas
Brain Sciences
AI
Habituation
Human Intelligence
Machine Intelligence
Social Intelligence
57
WI
?Brain Sciences
AI?
Hebbian learning
Spatial representations
Constraint satisfaction
Motivation
Population codes
Probabilistic reasoning
Learning
Habituation
Multi-agent
Multi-perception
Reasoning
Planning
Vision
Emotion
Attention
Knowledge based methods
Language
Memory and forgetting
58
WI
?Brain Sciences
AI?
Hebbian learning
Spatial representations
Constraint satisfaction
Motivation
Social networks
Population codes
Probabilistic reasoning
Learning
Community discovery
Small world theory
Habituation
Multi-agent
Multi-perception
Reasoning
Planning
Social dynamics
Social agents
Vision
Emotion
Attention
Knowledge based methods
Language
Memory and forgetting
Groupware
Social Intelligence
59

The Relationship Between WI BI
WI Systems
data sources and requirements
implementation
supports
verification
Brain Informatics
WI Fundamental
research needs
new models
60
From fMRI/EEG Experiments to New WI/Cognitive
Models
fMRI
New Cognitive Models
Data Mining/ Reasoning
measuring preprocessing
BI
Knowledge
Imaging
EEG
Data/ Knowledge Grid
Intelligent Infor. Analysis
AIIT
Wave
psychological
New WI Models
Wisdom Web
psychometrics
61
The synergy between WI and BI will yield
profound advances in our analyzing and
understanding of the mechanism of data,
knowledge, intelligence and wisdom, as well as
their relationship, organization and creation
process.

• Fundamental and implementation of WI will be
  studied as a central topic and in a unique way.
• It will fundamentally change the nature of IT in
  general and AI in particular.
• Towards human-level Web Intelligence

62
Our Recent Works

• Semantic Web mining/farming and automatic
  construction/management of ontologies
• Semantic social networks for intelligent portals
  
• Modeling/representing/managing user behavior
• PSML and distributed Web inference engine
• Wisdom Web based computing

63
Our Recent Works

• Semantic Web mining/farming and automatic
  construction of ontologies
• Semantic social networks for intelligent portal
• Modeling/representing/managing user behavior
• PSML and distributed Web inference engine
• Wisdom Web based computing

64
Existing Web Information Structures

• WI must study both portal-centralized and
  distributed information sources.
• Web information/knowledge could be
• either globally, distributed throughout the Web
  within multi-layer over the infrastructure of Web
  protocols,
• or locally, portal-centralized Web services
• (i.e. the intelligent service provider) that is
  integrated to its own cluster of specialized
  intelligent applications.

65
Web-based Problem-solving System

• To develop a Web-based problem-solving system for
  portal-centralized, query-answering based Web
  intelligent services/decision-making.
• The core of such a system is the PSML (Problem
  Solver Markup Language) and PSML-based
  distributed Web inference engines.

66
The Main Support Functions

• The expressive power and functional support
  (e.g. CSP, agents, combined network
  reasoning) in PSML for complex adaptive,
  distributed problem solving.
• Performing automatic reasoning on the Web by
  incorporating globally distributed contents and
  meta-knowledge automatically collected and
  transformed from the Semantic Web and social
  networks with locally operational knowledge/data
  bases.

67
The Main Support Functions (2)

• Representing and organizing multiple, huge
  knowledge/data sources for distributed network
  reasoning.
• Combining multiple reasoning methods in PSML
  representation and distributed inference engines,
  efficiently and effectively.
• Modeling user behavior and representing/managing
  it as a personalized model dynamically.

68
Distributed Query-Answering Model
comparing the speed of our own printer with other
companies products
users
query
answer
query decomposition
answer synthesis
sub-answer
sub-query
price
monoSpeed
resolution
...
WIE
WIE
WIE
cooperation
69
Distributed Web Structure
problem
Description
useful to the problem
Constraint
Nodei
Query
PSML
dividing the query into sub-queries
Meta-knowledge
classifying queries
Inference Engine
Meta-knowledge
Knowledge Base
Constraint Knowledge
case-based reasoning
Knowledge Grid
Domain Knowledge
Node
solving sub-queries
Su-Zheng-Zhong-Liu et al, 04
70
PSML and Web Inference Engine

• A feasible way as a step to implement such
  a PSML based Web Inference Engine is to
  combine OWL and Horn clauses plus agent
  technology (dynamic contents, distributed
  representation, meta-knowledge collection,
  and transformation agents).

71
Web Inference Engine
The Semantic Web Social Networks
Dynamic,Global Sources

MetaDATA Contents
MetaDATA Contents
MetaDATA Contents
OWL/XML
Transformation
Output
KAUS
Decision-making Support
KAUS
KB
DB
KB
DB
KB
DB
KB
DB
coupling
Static,Local Sources
72
Web Inference Engine
The Semantic Web Social Networks
Dynamic,Global Sources

MetaDATA Contents
MetaDATA Contents
MetaDATA Contents
OWL/XML
Transformation
Output
KAUS
Decision-making Support
KAUS
KB
DB
KB
DB
KB
DB
KB
DB
coupling
Static,Local Sources
73
Transformation Process
(Tomita-Zhong-Yamauchi04)
Multiple Portal Sites
A Printer Ontology (OWL)
Global
Local
Application-1 (unifying expressions)
Semantic unified content info. (RDF Schema)
Application-2 (transforming to KAUS)
Ontology for vocabulary unification (OWL)
KAUS sources
74
Web Inference Engine
The Semantic Web Social Networks
Dynamic,Global Sources

MetaDATA Contents
MetaDATA Contents
MetaDATA Contents
OWL/XML
Transformation
Output
KAUS
Decision-making Support
KAUS
KB
KB
DB
DB
KB
DB
KB
DB
coupling
Static,Local Sources
75
Next Questions

• How to design fMRI/EEG experiments to understand
  the principle of human reasoning and problem
  solving in depth?
• How to understand and predict user profile
  and behavior?
• How to implement human-level reasoning and
  problem solving on the Web?

76
Summary

• WI technologies will produce the new tools and
  infrastructure components necessary to create
  intelligent portals that serves its users
  wisely .
• New instrumentation (fMRI etc) and IT are causing
  an impending revolution in WI and Brian
  Sciences.
• The ultimate goal is to establish the foundations
  of Web Intelligence by studying Brain
  Informatics for developing Wisdom Web based
  intelligent systems that integrate all the
  human-level capabilities.

77
To Learn More about WI
wi-consortium.org
Books
Journals

• The IEEE/WIC/ACM
• International
• Conference on
• Web Intelligence
• AWIC
• IEEE-CS TCII

78
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79
The International WIC Institute (WICI)
the right group, at the right time
WIC-Australia Research Centre WIC-Beijing
Research Centre WIC-Canada Research Centre
WIC-France Research Centre WIC-HK Research
Centre WIC-India Research Centre
WIC-Japan Research Centre WIC-Korean Research
Centre WIC-Mexico Research Centre WIC-Poland
Research Centre WIC-Spain Research Centre
WIC-UK Research Centre
WIC
80
Thank You !
81
References Further Reading

• N. Zhong, J. Liu, Y.Y. Yao (eds.) Web
  Intelligence, Springer, March 2003
• J. Liu, N. Zhong, Y.Y. Yao, Z. Ras The Wisdom
  Web New Challenges for Web Intelligence (WI),
  Journal of Intelligent Information Systems, 20
  (1) 2003
• N. Zhong, J. Liu, Y.Y. Yao (eds.) IEEE Computer
  SI on Web Intelligence, 35 (11) (2002)
• N. Zhong, J. Liu, Y.Y. Yao In Search of The
  Wisdom Web, IEEE Computer, 35 (11) (2002)
• N. Zhong, Y.Y. Yao, J. Liu, S. Ohsuga (eds.) Web
  Intelligence Research and Development, LNAI
  2198, Springer (2001)
• N. Zhong, J. Wu, A. Nakamaru, M. Ohshima, H.
  Mizuhara Peculiarity Oriented fMRI Brain Data
  Analysis for Studying Human Multi-Perception
  Mechanism, The Cognitive Systems Research
  journal, Elsevier (2004)
• N. Zhong, Y.Y. Yao, M. Ohshima Peculiarity
  Oriented Multi-Database Mining,
  IEEE Transactions on Knowledge and Data
  Engineering, 15 (4) (2003)
• Y.Y. Yao, N. Zhong, J.J. Huang, C.X. Ou, C.N. Liu
  Using Market Value Functions for Targeted
  Marketing Data Mining, IJPRAI, 16 (8) (2002)
• Y. Sai, Y.Y. Yao, N. Zhong Data Analysis and
  Mining in Ordered Information Table, Proc.
  IEEE ICDM 2001
• N. Zhong, J.Z. Dong, S. Ohsuga Rule Discovery by
  Soft Computing Techniques, Neurocomputing, an
  international journal, 36 (1-4) (2001)
• SI on e-Services, CACM, Vol 46, No 6., June 2003
• SI on Service-Oriented Computing, CACM, Vol 46,
  No. 10, Oct 2003
• SI on e-Science, IEEE Intelligent Systems,
  Jan/Feb 2004

82
References Further Reading (2)

• N. Zhong Towards Web Intelligence, Proc.
  AWIC03 LNAI Springer (2003)
• Y.Y. Yao, N. Zhong, J. Liu, S. Ohsuga Web
  Intelligence (WI) Research Challenges and Trends
  in the New Information Age, in N. Zhong et al
  (eds) LNAI 2198 (2001)
• T. Berners-Lee, J. Hendler, O. Lassila, The
  Semantic Web, Scientific American, 284 (5)
  (2001)
• H.P. Alesso and C.F. Smith, The Intelligent
  Wireless Web, Addison-Wesley (2000)
• R. Kumar, P. Raghavan, S. Rajagopalan, A. Tomkins
  The Web and Social Networks, IEEE Computer
  Special Issue on Web Intelligence, 35 (11) (2002)
• A. Congiusta, A. Pugliese, D. Talia, P. Trunfio
  Designing Grid Services for Distributed
  Knowledge Discovery, Web Intelligence and Agent
  Systems, an international journal, IOS Press,
  1 (2) (2003)
• N. Zhong, C. Liu Dynamically Organizing KDD
  Processes, IJPRAI, World Scientific (2001)
• Y. Sai, Y.Y. Yao, N. Zhong Data Analysis and
  Mining in Ordered Information Tables, Proc.
  ICDM01 (2001)
• Y. Li, N. Zhong An Ontology-Based Web Mining
  Model TKDE (submitted)
• J.Z. Ji, C. Liu, Z. Sha, N. Zhong Personalized
  Recommendation Based on a Multilevel Customer
  Model, IJPRAI (submitted)
• J. Liu Web intelligence (WI) Some Research
  Challenge, Proc. IJCAI03.
• J. Liu, S. Zhang, J. Yang Characterizing Web
  Usage Regularities with Information Foraging
  Agents , TKDE, 16 (5) (2004)
• N. Zhong, J. Liu (eds.) Intelligent Technologies
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