Transcript Knowledge Technologies 2002-2006
Knowledge Technologies S
cope & focus in 2003
NCP meeting Jan 27-28, 2003, Brussels
Colette Maloney Interfaces, Knowledge and Content technologies, Applications & Information Market DG INFSO
Challenges
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information overload
• • • massive, heterogeneous data sets unstructured documents (eg e-mails)
new forms of content
• • software programs, sensors, ambient devices …
complex work processes
• • • collaborative work flows monitoring guidelines
corporate knowledge practices
• • the “zero-latency organisation”, shared KM
blur between content & services
• Napster: music or P2P?
Work-programme 2003-2004
Objective:
To develop semantic-based and context-aware systems to acquire, organise, process, share and use the knowledge embedded in multimedia content . Research will aim to maximise automation and services.
of the complete knowledge lifecycle and achieve semantic interoperability between Web resources
Theme Type Component Foundational level research research System level research Semantic-enabled systems and services Knowledge-based adaptive systems
Research theme #1
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Semantic-enabled systems & services
for the next-generation Web(s) • • semantic Webs within and across organisations, communities of interest …
smart Web services
• • automated, self-organising, robust & scaleable offering – – – – – networked knowledge discovery multimedia content mining content-based retrieval across heterogeneous databases, platforms & networks information visualisation …
Semantic-enabled systems and services
Human Human Machine-Machine Knowledge sharing Knowledge discovery Virtual Information and Knowledge Environments and the “Semantic Layer / Middleware” Documents Databases Email Web People Other Resources
Research theme #2
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Knowledge-based adaptive systems
• reasoning over / acting on • • large volumes of dynamic data and information • under uncertain or fuzzy boundary conditions, guidelines etc for • automated diagnosis & decision support • highly dynamic & time-critical applications • • modelling & optimisation “anytime-anywhere inferencing” 1934 2004
Knowledge-based adaptive systems
Smart product development. 2000-2002 DecisionCraft Analytics Ltd.
Send the snow-cats or not?
for industry, science, education … applications
Urban planning Accurately predicting arrival times for aircraft. - NASA - CTAS. Modelling finance markets Clinical guidelines support
KT - Basic research
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Foundational research
• • formal k- models, methods & languages ontology lifecycle (“ecology”) • • methods & tools for creating and maintaining extensible & interoperable ontologies – building domain/task specific ontologies – – bootstrapping broader, upper-level ontologies catering for multimedia & multilingual aspects standards for semantic interoperability • • between Web data, services & process descriptions between SemWeb, metadata & multimedia coding
Ontologies can vary enormously in size. Class, Property or Instance can range from 1-1000s...
KT – Component level research
• Component-level research into baseline functions & toolsets • • across media / content types within common reference architectures • • • • • • • • • automated knowledge acquisition semantic annotators intelligent Web scrapers or harvesters semantic search engines multimedia summarizers user-friendly editors visual assistants natural language tools (eg filtering & routing) …
KT – System level research
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System integration & validation
• tying together components into innovative end-to-end systems or services with • • • enhanced reasoning capability over large-scale & multi-dimensional more collaborative/community data sets knowledge sharing • addressing performance & effectiveness, user acceptance, ease of integration/customisation, impact on processes & legacy systems … • additionality of applicative showcases • • multi-sectoral (reusability & replicability) multi-lingual & multi-cultural
KT – System level research
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Candidate areas - purely indicative!
• • • • • • • scientific & technical resource discovery personal & collective memory systems multimedia content mining across the Web business intelligence technology watch corporate portals & intranets …
Should have multi-sector potential, in progressive areas - beyond state-of-the-art
Supporting issues
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Research infrastructure
• • • • metrics & benchmarking, test-bed data sets public domain ontologies & open source toolsets registries & locator services training (researchers, integrators, leading users) … •
Socio-economic issues
• • • usability, guides & best practice new business & revenue models awareness & user/supplier dialogue … •
Global reach
• international co-operation …
Summary
• The vision : the Web as a semantically-annotated resource shared by humans, software agents & networked devices • Two intertwined goals : • basic research : “understand” content, master knowledge embedded in multimedia objects • applied research : enable smarter, next-generation Web applications • From long-term research through to exemplary applicative showcases • Strong multidisciplinarity with many constituent disciplines & technologies; significant integration issues
What kind of project for KT?
IP NoE Foundational research Component level research System level integration
Yes Yes Yes Poss.
STRP
NR Poss. Yes
Poss. Yes
= Not recommended = Possibility, if clearly justified = Highly recommended
Yes NR Poss.
ideal IP for KT
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the “ideal” IP should encompass
• • • genuine research work “engineering” tasks (esp. methods & tools) system integration & validation (“total system” approach) •
along with
• • promotion & dissemination of results training, awareness & best practice (researchers, integrators, launching users) • cooperation & exchanges with related national and international efforts (incl. standards bodies) • socio-economic impact & consequences
Outcome of 2003 call
• •
fewer, bigger projects wrt. FP5 55+ meuro available:
• 4-5 IPs • 2-3 NoEs • 4-5 STRPs • 1-2 SSAs •
11-13 proposals likely to be retained
for funding … highly selective process!
• proposals cutting across knowledge / content / interface technologies are welcomed
Using the new instruments
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do not artificially create an IP!
• an IP should be THE project in the target area • an ambitious & progressive endeavour • with clearly defined milestones & checkpoints • appropriate use in this sector: not 30 Meuros, nor 3 Meuros; typically 6-12 Meuros, more where justified by scope & impact • an NoE should be interdisciplinary, include an industry section and / or a user section
Partnerships
• • • • •
consortium
• • •
IPs NoEs
7-10 partners, from 3+ countries 4 “core” partners min., from 3+ countries STRPs 4-6 partners, from 3+ countries
cohesive agenda; competent, committed & reliable partners complementarity: cover all areas you need duplication of competence
• • Necessary for NoEs Acceptable for IPs where dictated by project needs
industry/SME/academia/NAS participation: as dictated by project needs
Conclusion
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preserve your credibility: select one proposal and make it win!
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ensure that the proposal brings out key innovations
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full depth of participation rather than long list of organisation names
• critical mass: avoid the “1 FTE per partner” trap •
check relevance of your ideas with EC staff, at an early stage