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Spark, an application based on Serendipitous Knowledge Discovery.

T Elizabeth Workman1, Marcelo Fiszman1, Michael J Cairelli1

  • 1National Institutes of Health, National Library of Medicine, Lister Hill National Center for Biomedical Communications, Bethesda, MD, USA.

Journal of Biomedical Informatics
|January 7, 2016
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Summary
This summary is machine-generated.

This study introduces Spark, an application designed to enhance serendipitous knowledge discovery. It leverages user information-seeking behavior and semantic predications to foster unexpected learning.

Keywords:
Application developmentInformation-seeking behaviorKnowledge discovery

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Area of Science:

  • Information Science
  • Human-Computer Interaction
  • Knowledge Management

Background:

  • Information-seeking behavior is crucial for effective application design.
  • Serendipitous Knowledge Discovery (SKD) in online environments presents opportunities for learning.
  • Existing models like IF-SKD highlight the potential of integrating user behavior into application design.

Purpose of the Study:

  • To describe the Spark system, an application designed to facilitate serendipitous knowledge discovery.
  • To demonstrate how findings from information-seeking behavior research can inform application development.
  • To illustrate the application of semantic predications in designing for unexpected learning.

Main Methods:

  • System description of Spark, detailing its design and retrieval functionalities.
  • Utilizing data structures known as semantic predications.
  • Building upon the previously published IF-SKD model for online serendipitous knowledge discovery.

Main Results:

  • The Spark system is designed based on principles of information-seeking behavior.
  • Semantic predication graphs are generated to evoke serendipitous knowledge discovery.
  • The system's design and retrieval methods are tailored to enhance user learning experiences.

Conclusions:

  • Application development can be significantly informed by research on information-seeking behavior.
  • The Spark system provides a practical example of implementing serendipitous knowledge discovery.
  • Semantic predications offer a powerful approach for designing systems that foster unexpected insights.