IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-02918398.html
   My bibliography  Save this paper

How to Extract Knowledge from Professional E-Mails

Author

Listed:
  • François Rauscher

    (Tech-CICO - TECHnologies pour la Coopération, l’Interaction et les COnnaissances dans les collectifs - ICD - Institut Charles Delaunay - UTT - Université de Technologie de Troyes - CNRS - Centre National de la Recherche Scientifique - CNRS - Centre National de la Recherche Scientifique)

  • Nada Matta

    (Tech-CICO - TECHnologies pour la Coopération, l’Interaction et les COnnaissances dans les collectifs - ICD - Institut Charles Delaunay - UTT - Université de Technologie de Troyes - CNRS - Centre National de la Recherche Scientifique - CNRS - Centre National de la Recherche Scientifique)

  • Hassan Atifi

    (Tech-CICO - TECHnologies pour la Coopération, l’Interaction et les COnnaissances dans les collectifs - ICD - Institut Charles Delaunay - UTT - Université de Technologie de Troyes - CNRS - Centre National de la Recherche Scientifique - CNRS - Centre National de la Recherche Scientifique)

Abstract

Computer mediated communication is ubiquitous in Software design projects. Email is used for project coordination, but also for design, implementation and test. Especially with currents agile development methods, it is very common to interact through computer meditated communication like email, instant messaging and other collaborative tools in order to express functional needs, notify of issues and take appropriate decisions. In this paper we propose a Knowledge Trace Retrieval (KTR) system. It addresses the problem of retrieving elements of problem solving and design rationale inside business emails from a project. Even if knowledge management tools and practices are well spread in industry, they are rarely used for small projects. Our system aims at helping user retrieve traces of problem solving knowledge in large corpus of email from a past project. The framework and methodology is based on enhanced context (project data, user competencies and profiles), and use machine learning technics and ranking algorithm.

Suggested Citation

  • François Rauscher & Nada Matta & Hassan Atifi, 2015. "How to Extract Knowledge from Professional E-Mails," Post-Print hal-02918398, HAL.
    • Handle: RePEc:hal:journl:hal-02918398
    DOI: 10.1109/SITIS.2015.113
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-02918398. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.