Representing Scientific Knowledge

Explains the growth of scientific knowledge through diverse theoretical views and data-driven examplesDemonstrates the critical and fundamental role of a variety of uncertainties of scientific writing and scientific knowledgeIllustrates a solid set of visual analytic and text mining procedures and tools for active researchers Presents a framework of an ambitious research agenda, that may considerably increase the clarity of the status of the state of the art of scientific knowledge

Chaomei Chen is a Professor in the College of Computing and Informatics at Drexel University and a Professor in the Department of Library and Information Science at Yonsei University. He is the Editor in Chief of Information Visualization and Chief Specialty Editor of Frontiers in Research Metrics and Analytics. His research interests include mapping scientific frontiers, information visualization, visual analytics, and scientometrics. He has designed and developed the widely used CiteSpace visual analytic tool for analyzing patterns and trends in scientific literature. He is the author of several books such as Mapping Scientific Frontiers (Springer), Turning Points (Springer), and The Fitness of Information (Wiley). Min Song is an Underwood Distinguished Professor at Yonsei University. He has extensive experience in research and teaching in text mining and big data analytics at both undergraduate and graduate levels. Min has a particular interest in literature-based knowledge discovery in biomedical domains and its extensions to a broader context such as the social media. He is also interested in developing open source text mining software in Java, notably creating the PKDE4J system to support entity and relation extraction for public knowledge discovery.