Semantic Web for Generalized Knowledge Management Rudi Studer 1, 2, 3
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Transcript of Semantic Web for Generalized Knowledge Management Rudi Studer 1, 2, 3
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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 Karlsruhehttp://www.aifb.uni-karlsruhe.de/WBS
2 FZI Research Center on Information Technologies, Karlsruhehttp://www.fzi.de/wim
3 ontoprise GmbH, Karlsruhehttp://www.ontoprise.de
NSF-EU Workshop Semantic WebSophia AntibolisOctober 3-5, 2001
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AIFBAgenda
1. Knowledge Process: - Use: KM Applications (e.g. Portals)- Capture: Creation and Annotation of Metadata
2. Knowledge Meta Process- Ontology Learning
3. Conclusion
Use
Capture
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AIFB
Knowledge Meta Process &Knowledge Process
Knowledge Process
Working with KM Application
Knowledge Meta Process
Design, Implementation,
Maintenance
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AIFB
Retrieval /
AccessQuery
Search
Derive
Knowledge Process
CaptureExtract
Annotate
Create
Import
Documents
Metadata
Databases
Use
Apply
Summarize
Analyse
Automatic Use
Capture
Use
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AIFB
• 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”
KM ApplicationsUse
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AIFB
• 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
KM Applications: Anywhere and Anytime
Use
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AIFB
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
Knowledge PortalsUse
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AIFBKAON Portal Architecture
Use
Knowledge Warehouse
Clustering
Presentation Engine(RDF-)Crawler
Extractor
BrowserWWW / Intranet
Annotation Navigation Semantic Query
Person-alization
InferenceEngine
Semantic Ranking
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AIFB
Use
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AIFB
Use
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AIFB
• 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
Generating Knowledge PortalsUse
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AIFBAutomatically Generated Portals
Use
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AIFBCreation 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
Capture
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AIFB
• 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/
Creation and Generation of MetadataCapture
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AIFBKAON Annotation Environment
web pages
domain ontologies
copy
WWW
Document Management
Annotation Inference
Server
Informationextraction
Component
annotate
crawl
AnnotationTool GUI
plugin
plugin
plugin
OntologyGuidance
DocumentEditor
Annotation Environment
query
extract
crawl
annotatedweb pages
Capture
Functions:Knowledge Capturing + AnnotationAuthoring + Annotation
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AIFBKAON ONT-O-MAT
• Capturing and Annotation– Instance, relationship and attribute creation
– Document markup
• Authoring and Annotation– Document editing and markup
– Annotation on the fly
Capture
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AIFBFurther Issues
• 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
Capture
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AIFB
Knowledge Meta Process for Ontologies (cf. OTK-Project)
Revision and expansion based on feedback
Analyze usage patterns
Analyze competency questions
ONTOLOGY
Requirement specification
Analyze input sources
Develop baseline ontology
Concept elicitation with domain experts
Develop and refine target ontology
Manage organizational maintenance process
•GO / No GO decision
KickoffRefine-ment
Evaluation
Main-tenance &
Evolution
Feasi- bility Study
Ontology Learning
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AIFBOntology 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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AIFBOntology Learning:
Relation Miningroot
company
TK-company
Online servicecompany
Linguistically associated
Generate suggestion:
relation(company, company)
=> cooperateWith(company, company)
T-Online Nifty
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AIFBOntology 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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AIFBEvolution 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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AIFBKM 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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AIFBKM Applications and eLearning
• Edutella project exploits Semantic Web framework as a distributed query and search servicehttp://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, Germanyhttp://www.learninglab.de
• Institute AIFB is Learning Lab member