1. SEmantic Knowledge Technology http://sekt.semanticweb.org
SEKT
SEmantic Knowledge Technology

2. SEKT addressing the semantic knowledge technology research agenda
6th framework IP project
start date 1/1/2004
36 months, €12.5m
sekt.semanticweb.org

3. Key people Project Director – John Davies, BT
Technical Director – Rudi Studer, Karlsruhe
Project Manager – Paul Warren, BT
Project Management Board
Marko Grobelnik, JSI
Ralph Traphoener, Empolis
Hamish Cunningham, Sheffield
Juergen Angele, Ontoprise
Atanas Kiryakov, SIRMA AI
Jesus Contreras, iSOCO
Tom Boesser, kea-pro
Pompeu, UAB
Frank van Harmelen, VUA
Jos De Bruijn, DERI Innsbruck

4. The Goal
To deliver next generation semantic knowledge technology through:
Foundational research
(Semi-)automatic ontology generation and population
Ontology management (mediation, evolution, inferencing)
Innovative technology development
A suite of knowledge access tools
Open source ontology middleware platform
Validated by 3 case studies and benchmarking/usability activties
Supported by a methodology

5. XML is a first step Semantic markup Metadata
HTML  layout prescription
XML  content prescription
Metadata
within documents
not across documents
prescriptive

6. RDF, RDFS & OWL Standards of W3C Descriptive
RDF consisting of triples or sentences:
<subject, property, object>
<prod341, price, €54000>, <org176, sells, prod341>
RDF & RDFS used to define and populate ontologies
OWL – based on DL, more expressive, inference capabilities, 3 dialects

7. A (simple) example “Tolkien wrote ‘The Hobbit’ ”
hasWritten (‘
“A famous writer is a kind of writer”
subclassof(FamousWriter, Writer)
“ ‘The Hobbit’ is a book”
type(‘ ‘

8. Semantic Web & KM Making WWW information machine processable
annotation via ontologies & metadata
offers prospect of enhanced knowledge management
“Rank all the documents containing the word Tolkien”
“Show me the non-fiction books written by Tolkien about philology before 1940”
significant research & technology challenges are outstanding

9. Annotation is a potential bottleneck
… and how do we handle legacy knowledge?
We need automation:
semi-automatic learning of ontologies (KD)
semi-automatic generation of metadata (HLT)
maintaining and evolving ontologies (OMT)
a multi-disciplinary approach

10. Major RTD challenges
Improve automation of ontology and metadata generation
Research and develop techniques for ontology management and evolution
Develop highly-scalable solutions
Research sound inferencing despite inconsistent models
Develop semantic knowledge access tools
Develop methodology for deployment

11. Key outcomes
technological progress through development of leading edge, integrated semantically-enabled KM software tools
scientific progress through foundational research
creation of awareness via dissemination, training, case studies

12. Key outcomes
building the European Research Area in KM through collaboration with related IP and NoE projects in this area for a coordinated impact strategy
SEKT, DIP, KnowledgeWeb – SDK cluster
sdk.semanticweb.org
Collaboration with other projects – PASCAL, ALVIS, ECOLEAD, …

13. Multidisciplinary approach
KD/HLT
Management & evolution
KD/HLT
Need to determine appropriate technology mix
Semi-automatic

14. Human Language Technology
Aim:
bring together the current text-based web and the formal knowledge underlying Semantic Knowledge Technologies
increase the adaptivity of the metadata generation tools to evolving end-user information needs
Language processing tools
automating to a large degree the production of metadata
dealing with the large scale of the Web
supporting multiple languages
supporting learning from unlabeled data, using KD

15. Knowledge Access
context-aware tools for access to semantically-annotated knowledge
search, browse, visualise, summarise, share, infer
integrated into day-to-day business processes
automatic knowledge delivery based on current context (activity, location, device, interests)
support multiple end-user devices
also support for on-the-fly metadata creation
metadata creation as a side-effect of data creation

16. Feedback/forward – 3 case studies
helping newly-appointed judges
helping IT consultants
a corporate digital library
Use/refinement of SEKT methodology
Usability, business benefits and benchmarking

17. Resulting software should
Integrate with day-to-day business processes
automatic knowledge delivery based on current context and activity
Support on-the-fly metadata creation
metadata creation as a side-effect of data creation
Have a natural and intuitive user interface

18. Dissemination & Exploitation
SDK project cluster sdk.semanticweb.org
SEKT, DIP, KnowledgeWeb
1st European Semantic Web Symposium delivered
Multiple publications, press articles
Project poster, presentation, brochure
Exploitation
Systems integrator
Several software vendors
Sector-specific organisations
Open source v. software products

19. Project Overview

20. The inSEKTs Empolis University of Sheffield Universität Karlsruhe BT
Vrije Universiteit Amsterdam
Empolis
University of Sheffield
Universität Karlsruhe BT
Ontoprise
Kea-pro
Universität Innsbruck
iSOCO
Sirma AI
Universitat Autònoma
de Barcelona
Jozef Stefan Institute

21. Thank you for your time Any questions? john.nj.davies@bt.com
PAAM 96 Tutorial
Thank you for your time Any questions?
Copyright British Telecommunications plc 1996 31

23. Limitations of the Web today
Machine-to-human, not machine-to-machine