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Knowledge Technologies 2002-2006

Knowledge Technologies 2002-2006. S&T Content Scope & focus in 2003 Franco Mastroddi DG Information Society. Technology & usage shifts. IST today PC based Low bandwidth Content is linear <10% world population on-line Objects are just objects Search engines are word-based

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Knowledge Technologies 2002-2006

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  1. Knowledge Technologies2002-2006 S&T ContentScope & focus in 2003 Franco MastroddiDG Information Society

  2. Technology & usage shifts • IST today • PC based • Low bandwidth • Content is linear • <10% world population on-line • Objects are just objects • Search engines are word-based • Web “dumb, boring and isolated”* • “Ambient Intelligence” tomorrow • Devices, sensors, actuators • Broadband, wireless multimedia • Visual, intuitive, immersive content • >70% ? • Billions of tagged, networked objects • Context-based knowledge handling • The executable, extended X-Internet Sources: Forrester Ceo*, BTExact, NUAInternet, Gartner

  3. Challenges • 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?

  4. Socio-economic objectives • improve impact of KT on business & organisations • innovation, productivity & creativity • better exploitation of resources, tacit vs. explicit knowledge • improved methods & skills • automating knowledge flows • more collaborative processes, networked organisations & virtual knowledge communities • intelligent services & applications

  5. Key contributing developments • Semantic Web - plus multimedia • GRID / “X-Internet” • AI & knowledge representation • information / database methods • agents, multi-agent frameworks • “real-life” reasoning systems • machine learning • modelling & optimisation • natural language processing …

  6. (Draft) 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 of the complete knowledge lifecycleand achieve semantic interoperability between Web resources and services.

  7. Research theme #1 • Semantic-enabled systems & servicesfor 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 • …

  8. Virtual Information and Knowledge Environments and the “Semantic Layer / Middleware” Documents Databases Email Web People Other Resources Semantic-enabled systemsand services Human Human Machine-Machine Knowledge sharing Knowledge discovery

  9. Research theme #2 • Knowledge-based adaptive systems • reasoning over / acting on • large volumes of dynamic dataand information • under uncertain or fuzzy boundary conditions, guidelines etc • for • decision support • highly dynamic & time-critical applications • modelling & optimisation • “anytime-anywhere inferencing” 1934 2004

  10. 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. Clinical guidelines support Modelling finance markets

  11. KT - Basic research • 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...

  12. 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) • …

  13. KT – System level research • System integration & validation • tying together components into innovative end-to-end systems or services with • enhanced reasoning capability • over large-scale & multi-dimensional data sets • more collaborative/community 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

  14. KT – System level research • 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

  15. Supporting issues • 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 …

  16. Who has expressed interest? • 120+ EoI’s received for KT • 64% IPs, 36% NoEs (IST average) • focussing mostly on • Semantic Web technologies & intelligent Web services • knowledge modelling & reasoning in industry / science • average size consortium: 10-20 (IPs), 20-40 (NoEs) • 70% research organisations • “users” from • media & industry (eg product design, decision support) • information/content rich sectors (eg health, environment, transport) • Somewhat narrow NoEs • Relatively few “genuine” IPs …

  17. Conclusion • 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 Internet applications • From long-term research through to exemplary applicative showcases • Strong multidisciplinarity with many constituent disciplines & technologies; significant integration issues

  18. Stay in touch! • Main IST Web site: • www.cordis.lu/ist/fp6/fp6.htm • FP6 reference documents & guides: • www.cordis.lu/rtd2002/ • europa.eu.int/comm/research/fp6/networks-ip.html • Knowledge Technologies Web site: • www.cordis.lu/ist/ka3 • www.ktweb.org • National contact points: • www.cordis.lu/fp5/src/ncps.htm • EC staff in Luxembourg: • general: infso-kit@cec.eu.int • specific:franco.mastroddi@cec.eu.int

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