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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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Knowledge Meta Process &Knowledge Process

Knowledge Process

Working with KM Application

Knowledge Meta Process

Design, Implementation,

Maintenance

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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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• 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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• 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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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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Use

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Use

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• 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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• 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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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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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