TY - JOUR
T1 - Introducing the Big Knowledge to Use (BK2U) challenge
AU - Perl, Yehoshua
AU - Geller, James
AU - Halper, Michael
AU - Ochs, Christopher
AU - Zheng, Ling
AU - Kapusnik-Uner, Joan
N1 - Funding Information:
Research reported in this publication was supported by the National Cancer Institute of the NIH under award number R01CA190779. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
Publisher Copyright:
© 2016 New York Academy of Sciences.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - 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.
AB - 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.
KW - Big Data
KW - Big Knowledge
KW - clinical phenotyping
KW - drug–drug interactions
KW - summarization of knowledge
KW - visualization of knowledge
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U2 - 10.1111/nyas.13225
DO - 10.1111/nyas.13225
M3 - Article
C2 - 27750400
AN - SCOPUS:84991677434
SN - 0077-8923
VL - 1387
SP - 12
EP - 24
JO - Annals of the New York Academy of Sciences
JF - Annals of the New York Academy of Sciences
IS - 1
ER -