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

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

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

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

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”

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 ...

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

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

Use

Use

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

Automatically Generated Portals Use Automatically Generated Portals

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

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 http://ontobroker.semanticweb.org/annotation/ontomat/

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

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

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

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

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

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

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

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

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

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

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