1. 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
2 FZI Research Center on Information Technologies, Karlsruhe
3 ontoprise GmbH, Karlsruhe
NSF-EU Workshop Semantic Web
Sophia Antibolis
October 3-5, 2001

2. Agenda Knowledge Process: Knowledge Meta Process Conclusion
Use: KM Applications (e.g. Portals)
Capture: Creation and Annotation of Metadata
Knowledge Meta Process
Ontology Learning
Conclusion
Use
Capture

3. Knowledge Meta Process & Knowledge Process
Design, Implementation,
Maintenance
Knowledge Process
Working with KM Application

4. Knowledge Process Use Capture Documents Metadata Databases Use Create
Import
Documents
Metadata
Databases
Use
Apply
Summarize
Analyse
Automatic Use
Retrieval /
Access
Query
Search
Derive
Create
Capture
Extract
Annotate
Capture
Use

5. KM Applications Reduce overhead of applying KM
Use
KM Applications
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”

6. KM Applications: Anywhere and Anytime
Use
KM Applications:
Anywhere and Anytime
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
Wir befinden uns derzeit im Trend „alles, immer, überall“:
Das Internet wird mobil zugreifbar, und auf dem Markt tauchen
immer mehr persönliche "information appliances" wie drahtlos
vernetzte PDAs, WAP-fähige Handys oder elektronische
Bücher und Reiseführer auf. Ermöglicht wird dies primär
durch den weiter anhaltenden Fortschritt aller Zweige der
Informationstechnik hin zum "kleiner, billiger, leistungsfähiger".
Content ist kritisch!
Szenario erklären!
Ein mit den WebServices sehr verwandtes Gebiet ist die
Interoperbilität im B2B ...

7. Knowledge Portals Knowledge Portals are portals that ..
Use
Knowledge Portals
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

8. KAON Portal Architecture
Use
KAON Portal Architecture
Browser
WWW / Intranet
Presentation Engine
(RDF-)Crawler
Semantic
Ranking
Annotation
Semantic
Query
Person-
alization
Navigation
Extractor
Knowledge Warehouse
Inference
Engine
Clustering

9. Use

10. Use

11. Generating Knowledge Portals
Use
Generating Knowledge Portals
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

12. Automatically Generated Portals
Use
Automatically Generated Portals

13. Creation and Generation of Metadata
Capture
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

14. Creation and Generation of Metadata
Capture
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

15. Information extraction
Capture
KAON Annotation Environment
Annotation Environment
WWW
web pages
Document Management
copy
annotate
Annotation Tool GUI
plugin
Ontology
Guidance
Document
Editor
Annotation
Inference
Server
crawl
query
plugin
annotated
web pages
crawl
plugin
extract
domain
ontologies
Functions:
Knowledge Capturing + Annotation
Authoring + Annotation
Information extraction
Component

16. KAON ONT-O-MAT Capturing and Annotation Authoring and Annotation
Capture
Capturing and Annotation
Instance, relationship and attribute creation
Document markup
Authoring and Annotation
Document editing and markup
Annotation on the fly

17. Further Issues Semi-automatic generation of metadata for
Capture
Further 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

18. Knowledge Meta Process for Ontologies (cf. OTK-Project)
ONTOLOGY
Feasi- bility Study
Main-tenance &
Evolution
Refine- ment
Kickoff
Evaluation GO
/ No GO decision
Requirement specification
Analyze input sources
Develop baseline ontology
Concept elicitation with domain experts
Develop and refine target ontology
Revision and expansion based on feedback
Analyze usage patterns
Analyze competency questions
Manage organizational maintenance process
Ontology Learning

19. 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

20. Ontology Learning: Relation Mining
root
company
TK-company
Online service
T-Online
Nifty
Linguistically associated
Generate suggestion:
relation(company, company)
=> cooperateWith(company, company)

21. 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

22. 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

23. 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

24. 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

25. KM Applications and eLearning
Edutella project exploits Semantic Web framework as a distributed query and search service
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
Institute AIFB is Learning Lab member