Introducing the Big Knowledge to Use (BK2U) challenge

Yehoshua Perl, James Geller, Michael Halper, Christopher Ochs, Ling Zheng, Joan Kapusnik-Uner

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

The purpose of the Big Data to Knowledge initiative is to develop methods for discovering new knowledge from large amounts of data. However, if the resulting knowledge is so large that it resists comprehension, referred to here as Big Knowledge (BK), how can it be used properly and creatively? We call this secondary challenge, Big Knowledge to Use. Without a high-level mental representation of the kinds of knowledge in a BK knowledgebase, effective or innovative use of the knowledge may be limited. We describe summarization and visualization techniques that capture the big picture of a BK knowledgebase, possibly created from Big Data. In this research, we distinguish between assertion BK and rule-based BK (rule BK) and demonstrate the usefulness of summarization and visualization techniques of assertion BK for clinical phenotyping. As an example, we illustrate how a summary of many intracranial bleeding concepts can improve phenotyping, compared to the traditional approach. We also demonstrate the usefulness of summarization and visualization techniques of rule BK for drug–drug interaction discovery.

Original languageEnglish (US)
Pages (from-to)12-24
Number of pages13
JournalAnnals of the New York Academy of Sciences
Volume1387
Issue number1
DOIs
StatePublished - Jan 1 2017

All Science Journal Classification (ASJC) codes

  • Neuroscience(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • History and Philosophy of Science

Keywords

  • Big Data
  • Big Knowledge
  • clinical phenotyping
  • drug–drug interactions
  • summarization of knowledge
  • visualization of knowledge

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