Maps of the world are common in classroom settings. They are used to teach the juxtaposition of natural and political functions, mineral resources, political, cultural and geographical boundaries; occurrences of processes such as tectonic drift; spreading of epidemics; and weather forecasts, among others. Recent work in scientometrics aims to create a map of science encompassing our collective scholarly knowledge. Maps of science can be used to
see disciplinary boundaries; the origin of ideas, expertise, techniques, or tools; the birth, evolution, merging, splitting, and death of scientific disciplines; the spreading of ideas and technology; emerging research frontiers and bursts of activity; etc. Just like the first maps of our planet, the first maps of science are neither perfect nor correct. Today's science maps are predominantly generated based on English scholarly data: Techniques and procedures to achieve local and global accuracy of these maps are still being refined, and a visual language to communicate something as abstract and complex as science is still being developed. Yet, the maps are successfully used by institutions or individuals who can afford them to guide science policy decision making, economic decision
making, or as visual interfaces to digital libraries. This paper presents the process and results of creating hands-on
science maps for kids that teaches children ages 4-14 about the structure of scientific disciplines. The maps were tested in both formal and informal science education environments. The results show that children can easily transfer their (world) map and concept map reading skills to utilize maps of science in interesting ways.
This paper presents a novel approach to the visual exploration and navigation of complex association networks of biological data sets, e.g., published papers, gene or protein information. The generic approach was implemented in the SRS Browser as an alternative visual interface to the highly used Sequence Retrieval System (SRS) . SRS supports keyword-based search of about 400 biomedical databases. While the SRS presents search results as rank-ordered lists of
matching entities, the SRS Browser displays entities and their relations for interactive exploration. A formal usability
study was conducted to examine the SRS Browser interface's capabilities to support knowledge discovery and management.
This paper introduces a set of visualization tools that aim to support social navigation, the evaluation and optimization of three-dimensional virtual worlds and the study of their evolving communities. Previous work (Borner & Lin, 2001; Borner et al., 2002) demonstrated how this toolset can be used to analyze data acquired during a information treasure hunt. This paper extends and applies this tool set to visualize an entire virtual Conference on the topic Virtual Learning in Three Dimensions (VLearn3D) that was held in December 2002.
This paper describes the results of analysis and visualization of Animal Behavior research papers pulbished in major journals. Analysis is carried out to obtain associations among the citation records for the domain. The primary goal is to observe the content coverage and the prominent research areas and describe the research activities taking place over the past decade. The methodologies used to study the domain provide a potential platform that can be extended to cover the entire publication history of the domain. The fact that the field of Animal Behavior has remained uncharted to a great extent provides immense motivation for the study.
As more and more information is available on the Internet, search engines and bookmark tools become very popular. However, most search tools are based on character-level matching without any semantic analysis, and users have to manually organize their bookmarks or favorite collections without any convenient tool to help them identify the subjects of the Web pages. In this paper, we introduce an interactive tool that automatically analyzes, categorizes, and visualizes the semantic relationships of web pages in personal bookmark or favorites collections based on their semantic similarity. Sophisticated data analysis methods are applied to retrieve and analyze the full text of the Web pages. The Web pages are clustered hierarchically based on their semantic similarities. A utility measure is recursively applied to determine the best partitions that are visualized by what we call the Semantic Treemap. Various interaction methods such as scrolling, zooming, expanding, selecting, searching, filtering etc. are provided to facilitate viewing and querying for information. Furthermore, the hierarchical organization as well as the semantic similarities among Web pages can be exported and visualized in a collaborative 3D environment, allowing a group of people to compare and share each other's bookmarks.
The importance of information as a resource for economic growth and education is steadily increasing. Due to technological advances in computer industry and the explosive growth of the INternet much valuable information will be available in digital libraries. This paper introduces a system that aims to support a user's browsing activities in document sets retrieved from a digital library. Latent Semantic Analysis is applied to extract salient semantic structures and citation patterns of documents stored in a digital library in a comptutationally expensive batch job. At retrieval time, cluster techniques are used to organize retrieved documents into clusters according to the previously extracted semantic similarities. A modified Boltzman algorithm is employed to spatially organize the resulting clusters and their documents in the form of a 3D information landscape or 'i-scape'. THe i-scape is then displayed for interactive exploration via a multi- modal, virtual reality CAVE interface. Users' browsing activities are recorded and user models are extracted to give newcomers online help based on previous navigation activity as well as to enable experienced users to recognize and exploit past user traces. In this way, the system provides interactive services to assist users in the spatial navigation, interpretation, and detailed exploration of potentially large document sets matching a query.
SC521: Analyzing and Visualizing Knowledge Domains
This course introduces advanced data mining and information visualization techniques that can be used to support science and technology management. It demonstrates how large amounts of data, e.g., publication, patent, and grant data, can be analyzed, correlated, and visualized to map the semantic space of researchers, publications, funding, etc.. The resulting visualizations can be utilized to objectively identify major research areas, experts, institutions, grants, publications, journals, etc. in a research area of interest. In addition, they can assist to identify interconnections, the import and export of research between fields, the dynamics (speed of growth, diversification) of scientific fields, scientific and social networks, and the impact of strategic and applied research funding programs, among others.