Introducing the Big Knowledge to Use (BK2U) challenge

Abstract

The purpose of the Big Data to Knowledge (BD2K) 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 (BK2U). 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 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-based BK for drug–drug interaction discovery.

Opportunity recognition efficiency of small Chinese firms: findings from data envelopment analysis

“…Second, the firm should transfer its knowledge through channels with little interference, such as an effective intra-firm network (Argote and Ingram, 2000) and a work environment that encourages knowledge sharing (Hsieh et al , 2007). Third, it should enhance its members’ abilities to summarize, abstract and represent information with proper training (Perl et al , 2017). Only when the firm meets these requirements can its entrepreneurial members gain support from organizational knowledge and use it to recognize opportunities.…”

Data science, learning, and applications to biomedical and health sciences

“…The insights from Big Data have the potential to touch multiple aspects of health care and accelerate the translation of knowledge into the development of new predictive models and tools, resulting in improvement in delivery of care. Perl et al 14…”