Download intelligent content management system ist-2001

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Research in
Content and Knowledge Management
- ICONS Project
Prof David Bell
Queen’s University, Belfast
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ICONS formal information
• Rodan Systems - co-ordinator, initiator, project management,
architecture, prototype development, procedural knowledge
• Queen’s University /Univ of Ulster - knowledge management paradigms
• IPI Polish Academy of Sc - tools, standards, methods, user interface
• University of Dauphine - distributed content repository
• Universita della Calabria - inference machine Datalog
• Sema Group - development of the e-Government Portal
• InfoVide - ICONS deployment methodology
• Budget: > 3 million EURO; founded 1,9
• Duration: 24 months; effort 350 man-months
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ICONS Partners
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Rodan Systems S.A., Warsaw, Poland. Chief responsibility: project management, ICONS
architecture, prototype development. Contact persons: dr Witold Staniszkis, dr Bartosz Nowicki.
Website: www.rodan.pl. Corporate profile presentation: Rodan.ppt (PowerPoint, 1.573 KB).
Institute of Computer Science, The Polish Academy of Sciences (ICS), Warsaw, Poland. Chief
responsibility: assessment of tools, standards, and methods, advanced graphic user interface.
Contact person: prof. Kazimierz Subieta. Website: www.ipipan.waw.pl. Corporate profile
presentation: ICS_PAS.ppt (PowerPoint, 140 KB).
Centro di Ingegneria Economica e Sociale (CIES), Arcavacata di Rende, Italy. Chief
responsibility: multi-paradigm knowledge representation Contact persons: prof. Nicola Leone, prof.
Pasquale Rullo. Corporate profile presentation: CIES.ppt (PowerPoint, 147 KB).
Infovide SA, Warsaw, Poland. Chief responsibility: design and development of the "Structural Fund
Project Knowledge Portal". Contact person: Marcin Lewandowski. Website: www.infovide.pl.
Corporate profile presentation: Infovide.ppt (PowerPoint, 339 KB).
Sema Group Belgium, Brussels, Belgium. Chief responsibility: design and development of the
"Structural Fund Project Knowledge Portal". Contact persons: Jules Georges, Stoimir Djoudjev.
Website: be.sema.com. Corporate profile presentation: SchlumbergerSema.ppt (PowerPoint, 2.626
KB).
University Paris 9 Dauphine, Centre Des Etudes Et De Recherches En Informatique
Appliquee, Paris, France. Chief responsibility: distributed content repository. Contact person: prof.
Witold Litwin. Website: ceria.dauphine.fr. Corporate profile presentation: Dauphine.ppt (PowerPoint,
195 KB).
University of Ulster/Queen’s University Belfast,, U.K. Chief responsibility: multi-paradigm
knowledge representation. Contact person: Prof. David Bell. Web: http://www.qub.ac.uk/researchcentres/KDE/ Corporate profile presentation: Ulster.ppt (PowerPoint, 144 KB).
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Project goals
• Developing a stable prototype
• Demonstration of the viability of the ICONS prototype
in a real application – e-Government demonstrator
• Supporting uniform, knowledge-based access to
– distributed information resources available in the form of web
pages,
– pre-existing heterogeneous databases, as well as
– legacy information processing systems.
• Managing knowledge base comprising
– meta-information representing the domain ontology of various
nature (structural, procedural, declarative, knowledge maps)
– multimedia content
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Specific Issues QUB are addressing
Prof David Bell
Yaxin Bi
Dr Hui Wang
Gongde Guo
QUB and UU
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Introduction
• Knowledge Representation
• Collaboration
• Information Integration
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1. Knowledge Representation (D-S)
• Dempster-Shafer Theory
– Data Representation using Dempster-Shafer theory
• Extended relational database model
– Evidence and Rules
 Rule Strengths
 Data and Evidence
 From Evidence Strengths And Rule Strengths To Hypothesis Strength
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A Well-known Example:
The universal set is the set of all possible
conclusions.
A particular piece of evidence supports a Subset of
these.
eg. Evidence from a lab supports conclusion that
Tom’s disease is Cirrhosis of the liver or Hepatitis
to the degree 0.60.
This can be combined with other evidence and the information can
be stored in an extended relational model eg. in test 2 relation below
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Extending the Relational Model to
support Dempster-Shafer Methods
Test 1
Patient
Name
Test Name ……. Result
……..
…….
……. …….
Tom
Lab
……
<{Cirr,Hep}0.6,>,<,0.4>
Tom
Image
……
<{Cirr,Hep,Pan},0.7>,<,0.3>
……
……
……
……
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Combining these 2 pieces of
evidence…..
Lab
This gives:
.
60
{C,H}
.40

.70
{C,H,P}
.42
{C,H}
.28
{C,H,P}
.30

.18
{C,H}
.12

Image
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m({C,H}) = .60
m({C,H,P}) = .28
m()
= .12
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Test 2
Patient Name
……..
Result
…….
……
……..
Tom
…….
…….
…….
<{Cirr,Hep,Pan},0.28>,
<{Cirr,Hep},0.60>,<,0.12>
…….
…….
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Weighing Evidence
Use with Data Mining
(eg Text Categorisation)
and/or Risk Analysis?
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Knowledge Representation
(some other paradigms)
• Bayesian Belief Networks
– Generating causal trees
– Reasoning using a causal tree with activated data
 Belief updating
 Belief propagation
 The PropBel algorithm
• Hyperrelations used for representing mined knowledge
TD
HR
TF
WR
WW
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2.Collaboration
• Data Mining
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Clustering
Classification/Categorisation
Rule Extraction
Summarization
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Architecture of Text Categorization
Predefined
Document
Document
Converting
Function word
Removal
Word
Stemming
Classifier
Construction
Feature
weighting
Unclassified
Documents
Document
Categorization
Feature
Selection
Dictionary
Construction
Data Input
TC Engine
Write the Results
into XML File
Data Pre-processing
The arrow with dash line represents the data flow in categorization process as the
arrow with solid line represents the data flow in classifier construction process
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Implementation
• Implement a platform for text categorization
• Implement a k-NN Model classification algorithm
• Integrate three classification algorithms,
including SVM, KNN and ROCCHIO
• Support multi-types of documents
• Support multi-language documents
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Evaluation
Category
SVM
K-NN
Rocchio
kNNModel
Alt.atheism
94.85
88.67
83.85
91.38
…
…
…
…
…
Sci.cry
94.33
89.16
89.43
88.61
sci.med
93.91
88.40
88.22
90.54
Sci.space
89.80
83.45
86.76
86.83
Soc.religion.christian
86.70
83.17
81.00
83.33
Talk.politics.guns
93.64
8905
85.51
88.94
Talk.politics.mideast
96.12
91.39
93.09
91.54
Talk.politics.misc
87.38
85.11
75.37
83.29
Talk.religion.misc
88.38
80.38
74.18
79.47
macroaveraged F1
83.60
79.07
78.80
80.79
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3. Intelligent Agents
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Integration
Intelligent, web services based agents
a software entity that carries
out some set of operations
on behalf of a user or
another program with some
degree of independence or
autonomy, and in so doing,
employing some knowledge
or representation of the
user’s goals or desires
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Implementation of the IADE
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An Intelligent Agent Development Environment has been implemented which
is built on the JADE agent platform
Two applications – Communities of Practice and Web Services, as part of a
knowledge management system.
Web Services return explicit knowledge from which a user can formulate a
request for tacit knowledge to be returned by an expert through the CoP
applications.
Admin and user GUI has been written to allow admin of the agents and
ontology manipulation for Web Services.
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Two kinds of Agent
Answering hard questions from user
Helping user see more
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