Knowledge visualization: from theory to practice

Visualizations have been known as efficient tools that can help users analyze complex data. However, understanding the displayed data and finding underlying knowledge is still difficult. In this work, a new approach is proposed based on understanding the definition of knowledge. Although there are many definitions used in different areas, this work focuses on representing knowledge as a part of a visualization and showing the benefit of adopting knowledge representation. Specifically, this work begins with understanding interaction and reasoning in visual analytics systems, then a new definition of knowledge visualization and its underlying knowledge conversion processes are proposed. The definition of knowledge is differentiated as either explicit or tacit knowledge. Instead of directly representing data, the value of the explicit knowledge associated with the data is determined based on a cost/benefit analysis. In accordance to its importance, the knowledge is displayed to help the user understand the complex data through visual analytical reasoning and discovery.