Presentation is loading. Please wait.

Presentation is loading. Please wait.

AIFB 1 Semantic Web for Generalized Knowledge Management Rudi Studer 1, 2, 3 Siggi Handschuh 1, Alexander Maedche 2, Steffen Staab 1, 3, York Sure 1 1.

Similar presentations

Presentation on theme: "AIFB 1 Semantic Web for Generalized Knowledge Management Rudi Studer 1, 2, 3 Siggi Handschuh 1, Alexander Maedche 2, Steffen Staab 1, 3, York Sure 1 1."— Presentation transcript:

1 AIFB 1 Semantic Web for Generalized Knowledge Management Rudi Studer 1, 2, 3 Siggi Handschuh 1, Alexander Maedche 2, Steffen Staab 1, 3, York Sure 1 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 AIFB 2 Agenda 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

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

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

5 AIFB 5 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 Applications Use

6 AIFB 6 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

7 AIFB 7 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 Portals Use

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

9 AIFB 9 Use

10 AIFB 10 Use

11 AIFB 11 Exploit ontologies and related metadata –Various conceptual models are needed, a.o. Application domain Task at hand User profile Several approaches under development –Stanfords OntoWebber –Karlsruhes KAON-Portal FZIBroker as one instantiation –Integrate browsing, querying, content providing Generating Knowledge Portals Use

12 AIFB 12 Automatically Generated Portals Use

13 AIFB 13 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 Capture

14 AIFB 14 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 Creation and Generation of Metadata Capture

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

16 AIFB 16 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 Capture

17 AIFB 17 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 Capture

18 AIFB 18 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 Kickoff Refine- ment Evaluation Main- tenance & Evolution Feasi- bility Study Ontology Learning

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

21 AIFB 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 AIFB 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 AIFB 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 AIFB 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 AIFB 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

Download ppt "AIFB 1 Semantic Web for Generalized Knowledge Management Rudi Studer 1, 2, 3 Siggi Handschuh 1, Alexander Maedche 2, Steffen Staab 1, 3, York Sure 1 1."

Similar presentations

Ads by Google