CORONA - QURATOR - PANQURA - a technology platform for more information transparency in times of crisis; subproject 6: Aspect-oriented knowledge analysis and context-sensitive, explorative information search.

  • Funded by Bundesministerium für Bildung und Forschung [German Federal Ministry of Education and Research] (BMBF)
  • Total publications:0 publications

Grant number: 03COV03F

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Key facts

  • Disease

    COVID-19
  • Start & end year

    2021
    2022
  • Known Financial Commitments (USD)

    $621,422.4
  • Funder

    Bundesministerium für Bildung und Forschung [German Federal Ministry of Education and Research] (BMBF)
  • Principal Investigator

    Adrian Paschke
  • Research Location

    Germany
  • Lead Research Institution

    Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung eingetragener Verein
  • Research Priority Alignment

    N/A
  • Research Category

    Policies for public health, disease control & community resilience

  • Research Subcategory

    Communication

  • Special Interest Tags

    N/A

  • Study Type

    Non-Clinical

  • Clinical Trial Details

    N/A

  • Broad Policy Alignment

    Pending

  • Age Group

    Not Applicable

  • Vulnerable Population

    Not applicable

  • Occupations of Interest

    Not applicable

Abstract

The goal of the subcomplex "Aspect-Oriented Knowledge Analysis" is to develop a semi-automatic procedure for recognizing contexts in which formalized knowledge (facts and axioms) are valid. E.g., the proposition "wearing a mouth guard does not provide substantial protection against transmission of the Corona virus" expressed in online sources does not represent an objectively true fact but a proposition that arose and may be refuted in the context of specific scientific research activities. Context is important to objectify facts, allow end users to categorize and evaluate information, and resolve filter bubbles. Context can be the scientific source, but it can also be temporal, geographic, political, religious, and so on. The second complex "context-sensitive exploratory information search" builds on the first using learned contexts formalized in AspectOWL as additional input for a semantic search. The goal is to sort the search results according to the user's needs, taking context into account, and to present the context in a suitable way to explain and classify the search results for the user.