rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Representing Scientific Knowledge
    sub title: The Role of Uncertainty
  name:
    name part: Chen, Chaomei.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Song, Min.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2017
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XXXII, 375 p. 200 illus., 165 illus. in color. online resource.
  abstract: This book is written for anyone who is interested in how a field of research evolves and the fundamental role of understanding uncertainties involved in various stages of the development of a scientific domain. In a nutshell, the uncertainty of scientific knowledge is how much we really know what we think we know. We introduce a series of computational and visual analytic techniques from research areas such as science mapping, text mining, literature-based discovery, and semantic network analysis so that readers can apply these tools to the study of a subject matter of their choice. In addition, we set the diverse set of methods in an integrative context that draws upon insights from philosophical, sociological, and evolutionary theories of what drives the advances of science so that the readers of the book can guide their own research with their enriched theoretical foundations. Scientific knowledge is complex. A subject matter is typically built on its own set of concepts, theories, methodologies, and findings discovered by generations of researchers and practitioners. Scientific knowledge changes constantly. Some changes are profound and long-lasting, whereas others may be transient. How can we keep abreast of the state of the art as science advances? How can we effectively and precisely capture the epistemic status of the current science? The study of scientific knowledge in general has been overwhelmingly focusing on scientific knowledge per se. In contrast, the epistemic status of scientific knowledge at various levels of granularity has been largely overlooked, especially when the focus is on the development of a scientific domain. This book aims to highlight the role of uncertainties in developing a better understanding of the status of scientific knowledge at a particular time and how its status evolves over the course of the development of research. Furthermore, we demonstrate how the knowledge of the types of uncertainties associated with scientific claim…
  note: by Chaomei Chen, Min Song.
  subject:
    topic: Computer science
  subject:
    topic: Software engineering
  subject:
    topic: Data mining
  subject:
    topic: Computer graphics
  subject:
    topic: Computer Science
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Computer Imaging, Vision, Pattern Recognition and Graphics
  subject:
    topic: Software Engineering
  classification: QA76.9.D343
  classification: 006.312
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783319625430
  identifier: http://dx.doi.org/10.1007/978-3-319-62543-0
  record info:
    record creation date: 171127
    record change date: 20171127095940.0
    record identifier: 978-3-319-62543-0