Semantic Web for Generalized Knowledge Management Rudi Studer 1, 2, 3

1. Semantic Web for Generalized Knowledge Management RudiStuder1, 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, Karlsruhehttp://www.fzi.de/wim 3 ontoprise GmbH, Karlsruhehttp://www.ontoprise.de NSF-EU Workshop Semantic Web Sophia Antibolis October 3-5, 2001

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

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

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

5. 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. 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

7. 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 accessto and • interaction possibilities with • the contents of the portal • cf. OntoWeb portal

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

9. Use

10. Use

11. 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. Use Automatically Generated Portals

13. 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 • Authoringof new documents (get annotation for free) • Reuse existing structured data, e.g. available in databases • KAON Reverse tool

14. 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 athttp://ontobroker.semanticweb.org/annotation/ontomat/

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

16. 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

17. 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. Ontology Learning 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

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. root company TK-company Online service company Ontology Learning: Relation Mining Nifty T-Online 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 metadatais 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 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