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Laboratoire d'InfoRmatique en Image et Systèmes d'information

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Laboratoire d'InfoRmatique en Image et Systèmes d'information
UMR 5205 CNRS / INSA Lyon / Université Claude Bernard Lyon 1 / Université Lumière Lyon 2 / École Centrale de Lyon
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Axis 1 : Data, Document, Knowledge (D2C)

Coordinator Sylvie Calabretto
Beginning date Jan 01, 2003 - Ending date Sep 30, 2006
Presentation News Publications
Topics of Interest

Topics of Interest

Omnipresent information systems provide support for extremely various and numerous society's activities. We identify as a challenge that information "makes sense" for users needs and/or for users interactions. Users (and more and more software agents) can exploit available resources as knowledge or "intelligent" services in the context of their current task.

Our axis proposes theoretical and practical work with the objective of tackling this challenge up by trying to answer the question: "How can we make knowledge and/or intelligent behaviors emerge from (networked) computers?"

Common research aims are discovering, managing, exploiting, sharing, representing knowledge as it is expressed in what we propose to call "traces" that document the computer users' tasks. These "traces" are knowledge containers which can have various forms:

  • Documents in the general meaning of the word, especially digital documents,
  • Databases content
  • System-user interactions traces
  • Agents interactions traces (when mediated by computers)
  • Usage traces as they can be discovered in large databases (profiles)
  • Complex codes as they can be discovered in huge databases (genomics, &x2026;)
  • etc.

We plan to address a limited number of issues:

  • Managing digital documents with respect to their multiple structures
  • Facing the inherent structural and semantic heterogeneity of knowledge containers
  • Developing efficient and generic assistants, especially for human learning tasks
  • Modeling, analyzing and representing interactions in collectivities
  • Designing languages for representing web semantics
  • Facing the complexity of information access by estimating confidence and costs of knowledge containers
  • Extracting patterns from huge data bases allowing emergence of new knowledge

The axis is organized in four sub-axis:

  • Semantic modeling of documents
  • Cognition, experience and situated agents
  • Knowledge representation, reasoning and databases
  • Knowledge discovery


Document multistructures, annotations models, usage traces, human learning, interactions in collectivities, languages for expressing semantics, semantic web, pattern extraction from huge databases, inductive query optimisation

Last update : 2006-08-31 11:07:46