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AIFB
Semantic Web for Generalized
Knowledge Management
Rudi Studer1, 2, 3
Siggi Handschuh1, Alexander Maedche2,
Steffen Staab1, 3, York Sure1
1 Institute AIFB, University of Karlsruhe
http://www.aifb.uni-karlsruhe.de/WBS
2 FZI Research Center on Information Technologies, Karlsruhe
http://www.fzi.de/wim
3 ontoprise GmbH, Karlsruhe
http://www.ontoprise.de
NSF-EU Workshop Semantic Web
Sophia Antibolis
October 3-5, 2001
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AIFB
Agenda
1. Knowledge Process:
-
Use
Use: KM Applications (e.g. Portals)
Capture: Creation and Annotation of Metadata
Capture
2. Knowledge Meta Process
-
Ontology Learning
3. Conclusion
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AIFB
Knowledge Meta Process &
Knowledge Process
Knowledge Meta Process
Design, Implementation,
Maintenance
Knowledge Process
Working with KM Application
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Apply
Summarize
Analyse
Automatic Use
Use
Use
AIFB
Knowledge Process
Documents
Metadata
Databases
Create
Import
Query
Search
Derive
Retrieval /
Access
Capture
Capture
Extract
Annotate
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KM Applications
AIFB
Use
• Reduce overhead of applying KM
– Seamless integration of KM application into
working environment
– Exploit existing legacy data, e.g. databases
• Avoid information overload
– Context-dependent access and presentation
of knowledge
• Reflect task at hand
• Reflect used output device
– Personalized access and presentation
• Exploit user profile
• Be able to “forget”
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KM Applications:
Anywhere and Anytime
AIFB
Use
• Anywhere and anytime access to knowledge
• Intranet environment
• Internet environment
• Laptop/PDA/Mobile phone
• Wearable devices
• What you get presented
• is what you need
• is tailored to your profile
• is adapted to the output
device
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Knowledge Portals
AIFB
Use
Knowledge Portals are portals that ..
• focus on the generation, acquisition,
distribution and the management of
knowledge
• in order to offer their users
high-quality access to and
interaction possibilities with
the contents of the portal
• cf. OntoWeb portal
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Use
AIFB
KAON Portal Architecture
Browser
WWW / Intranet
Presentation Engine
(RDF-)Crawler
Annotation
Navigation
Semantic
Query
Semantic
Ranking
Personalization
Extractor
Knowledge Warehouse
Clustering
Inference
Engine
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AIFB
Use
9
AIFB
Use
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Generating Knowledge Portals
AIFB
Use
• Exploit ontologies and related metadata
– Various conceptual models are needed, a.o.
• Application domain
• Task at hand
• User profile
• Several approaches under development
– Stanford’s OntoWebber
– Karlsruhe’s KAON-Portal
• FZIBroker as one instantiation
– Integrate browsing, querying, content providing
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Automatically Generated Portals
AIFB
Use
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AIFB
Capture
Creation and Generation
of Metadata
• Manual creation of metadata for web documents is a
time-consuming process
• Possible solutions:
– Process web documents and propose annotations to the
annotator
• Use information extraction capabilities based on simple
linguistic methods
• Exploit domain specific lexicon and ontology to bridge the
gap between linguistic and conceptual structures
– Authoring of new documents (get annotation for free)
– Reuse existing structured data, e.g. available in databases
• KAON Reverse tool
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AIFB
Capture
Creation and Generation
of Metadata
• Methods are currently under development in the
DAML OntoAgents project
– Cooperation project
• Stanford University, DB Group (Stefan Decker)
• Univ. of Karlsruhe, Institute AIFB
• KAON Annotation Environment combines
– Manual creation of metadata
– Semi-automatic generation of metadata
– metadata-based authoring
• Partially realized in the KAON ONT-O-MAT tool,
available for download at
http://ontobroker.semanticweb.org/annotation/ontomat/
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KAON Annotation Environment
Annotation Environment
Document
Management copy
Annotation
Tool GUI
AIFB
Capture
WWW
web
pages
annotate
plugin
query Annotation crawl
Ontology Document
plugin
Inference
Guidance Editor
Server
crawl
plugin
extract
Functions:
Knowledge Capturing + Annotation
Authoring + Annotation
Information
extraction
Component
annotated
web pages
domain
ontologies
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AIFB
Capture
KAON ONT-O-MAT
• Capturing and Annotation
– Instance, relationship and attribute creation
– Document markup
• Authoring and Annotation
– Document editing and markup
– Annotation on the fly
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Further Issues
AIFB
Capture
• Semi-automatic generation of metadata for
–
–
–
–
Text documents
Images
Videos
Audio
• Combine multimedia standards with Semantic Web
technologies
– MPEG-7, SMIL
– RDF schema, OIL, DAML-OIL
• Achieve semantic interoperability between
different standards
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AIFB
Knowledge Meta Process for
Ontologies (cf. OTK-Project)
ONTOLOGY
Feasibility
Study
•GO
/ No GO
decision
Kickoff
Refinement
Evaluation
Requirement
specification
Concept
Revision and
elicitation with expansion
domain
based on
feedback
Analyze input experts
Develop and
Analyze usage
sources
refine target
patterns
Develop
ontology
Analyze
baseline
competency
ontology
questions
Ontology
Learning
Maintenance &
Evolution
Manage
organizational
maintenance
process
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AIFB
Ontology Learning
• Lots of ontologies have to be built
• Ontology engineering is difficult and time-consuming
– Cf. tools OntoEdit, Protégé-2000, OilEd
• Solution:
– Apply Machine Learning to ontology engineering
• Multi-strategy learning
• Exploit multiple data sources
• Build on shallow linguistic analysis
– Build the ontology in an application-oriented way, based on
existing resources
• Reverse Engineering
– Combine manual construction and learning into a
cooperative engineering environment
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AIFB
Ontology Learning:
Relation Mining
root
company
TK-company
Online service
company
Nifty
T-Online
Linguistically associated
Generate suggestion:
relation(company, company)
=> cooperateWith(company, company)
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AIFB
Ontology Learning: Emergent
Semantics
• Derive consensual conceptualizations in
a bottom-up manner
• Exploit interaction in a decentralized environment
– Peer-to-peer scenario
– Hundreds of local ontologies
– Learn alignment of ontologies through usage
• One approach within a multi-strategy environment
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AIFB
Evolution of Ontology-based
KM Applications
• Real world environment is changing all the time:
–
–
–
–
new businesses
new organizational structures in enterprises
new products and services
...
• Ontologies have to reflect these changes
– new concepts, relations and axioms
– new meanings of concepts
– concepts and relationships become obsolete
• Support for evolution of ontologies and metadata
is essential
– ontology-based applications depend on
up-to-date ontologies and metadata
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AIFB
Conclusion
• Semantic Web provides promising way for providing
relevant knowledge
• Appropriate granularity
• Personalized presentation
• Task- and location-aware
• Reduce overhead of …
– building up and
– maintaining KM applications
=> most critical success factor for real-life applications
(IT aspect)
• Reduce centralization caused by ontology-based
approaches
– Use multiple ontologies
– Combine top-down and bottom-up approaches
for ontology construction and learning
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AIFB
KM Applications and eLearning
• KM application has to be embedded into
a learning organization
• eLearning fits smoothly into such an environment
– Task driven learning
– Learning based on competence analysis
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AIFB
KM Applications and eLearning
• Edutella project exploits Semantic Web framework as
a distributed query and search service
http://sourceforge.net/projects/edutella/
– Peer-to-peer service for the exchange of educational
metadata
– Part of PADLR project (Personalized Access to Distributed
Learning Repositories)
– Cooperation between Stanford University and
Learning Lab Lower Saxony (L3S), Hannover, Germany
http://www.learninglab.de
• Institute AIFB is Learning Lab member
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