The Third International Symposium on
Knowledge Domain Visualization (KDViz'04)
In the 8th International Conference on Information Visualization
Chaomei Chen (
Katy Borner (
Extended Deadline: March 31, 2004 .
Jasna Kuljis (
Loet Leydesdorff (The
Xia Lin (USA)
Steven Morris (USA)
Andre Skupin (USA)
Henry Small (USA)
Anthony van Raan (The Netherlands)
Knowledge Domain Visualization (KDViz) aims to improve our understanding of the development of a knowledge domain through the study of a wide variety of quantitative and qualitative properties of a knowledge domain. KDViz emphasizes the great potential of an approach that integrates techniques such as information visualization, exploratory data analysis, information retrieval, and information science.
The KDViz Symposium series aims to provide an inter-disciplinary forum for researchers and practitioners from a wide variety of disciplines to address theories, methodologies, techniques, applications, evaluations and case studies in relation to KDViz. The symposium also aims to promote the cross-disciplinary awareness between disciplines such as information visualization and information science. For the purpose of this symposium, a knowledge domain is broadly defined as a dynamic, evolving intellectual structure of a given subject matter. Knowledge domain visualization aims to reveal the dynamics of a knowledge domain by utilizing a wide variety of techniques involving visual thinking, visual discovery, visual exploration, and visual analysis.
The symposium will seek original papers concerning, but not limited to, the following topics. Submitted papers must clearly demonstrate a connection between information visualization and the study of a knowledge domain:
Fundamentals of KDViz
· Case Studies
· Citation Analysis
· Domain Analysis and Modeling
· Historical, Sociological, or Philosophical Approaches
· Knowledge Discovery, Knowledge Representation, and Knowledge Diffusion
· Invisible Colleges, Scientific Networks, Social Networks, Scientific Paradigms
· Qualitative and Quantitative Methodologies
· Dynamic Models of Scientific Disicplines
· Growth Models of Science and Technology
A major goal of the symposium is to demonstrate and compare different techniques, algorithms, and approaches that can be utilized to analyze and visualize knowledge domains. Participants of the symposium are encouraged to use the following datasets in their studies. Follow the links below for detailed descriptions of these datasets and downloading them.
Additional opportunities of publication include journal special issues and an edited book.
Instructions for Authors:
Dr Chaomei Chen
College of Information Science and Technology
Philadelphia, PA 19104-2875, USA
Email: Chaomei.Chen (at) cis.drexel.edu
Dr Katy Börner
Information Science & School of Informatics,
Indiana University, 10th Street & Jordan Avenue, Bloomington,
IN 47405, USA
Phone: +01 812 855-3256,
Fax: +01 812 855-6166
E-mail: katy (at) indiana.edu