title: Introducing the Big Knowledge to Use (BK2U) challenge.
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.
authors.affiliation: Department of Computer Science.
authors.forename: Yehoshua
authors.initials: Y
authors.lastname: Perl
authors.affiliation: Department of Computer Science.
authors.forename: James
authors.initials: J
authors.lastname: Geller
authors.affiliation: Information Technology Department, New Jersey Institute of Technology, Newark, New Jersey.
authors.forename: Michael
authors.initials: M
authors.lastname: Halper
authors.affiliation: Department of Computer Science.
authors.forename: Christopher
authors.initials: C
authors.lastname: Ochs
authors.affiliation: Department of Computer Science.
authors.forename: Ling
authors.initials: L
authors.lastname: Zheng
authors.affiliation: First Databank Inc, San Francisco, California.
authors.forename: Joan
authors.initials: J
authors.lastname: Kapusnik-Uner
mesh_terms: D000818:Animals; D019295:Computational Biology / Q000379:methods* / Q000639:trends; D057225:Data Mining / Q000379:methods / Q000639:trends; D003658:Decision Making, Computer-Assisted; D004347:Drug Interactions; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D007091:Image Processing, Computer-Assisted; D020300:Intracranial Hemorrhages / Q000145:classification* / Q000453:epidemiology / Q000209:etiology / Q000503:physiopathology; D051188:Knowledge Bases; D008959:Models, Neurological; D004364:Pharmaceutical Preparations / Q000145:classification; D039061:Systematized Nomenclature of Medicine; D009626:Terminology as Topic; D057170:Translational Research, Biomedical / Q000379:methods* / Q000639:trends
chemical_list: D004364:Pharmaceutical Preparations
keywords: Big Data; Big Knowledge; clinical phenotyping; drug-drug interactions; summarization of knowledge; visualization of knowledge