Science and technology interactions discovered with a new topographic map-based visualization tool
Filip Deleus, Marc M. Van Hulle · 2001
A new tool for discovering and visualizing interactions between scientific publication domains and industrial patent domains is introduced. The tool is applied to a database of the United States patent data (USPTO) from 1980 till 1995 and the Science Citation Index (SCI) bibliographic databases. The core algorithm behind our tool is the kernel-based Maximum Entropy Rule (kMER), a learning scheme for developing topographic maps of formal neurons. The Gaussian kernels that correspond to each neuron are used for constructing a density map on which a hillclimbing algorithm is applied for locating and visualizing clusters in the topographic map. For each set of clustered data, a new topographic map is developed, and so on. A new method is introduced in order to visualize this hierarchical structure into a single condensed cluster map. The procedure is applied to patent and bibliographic data separately in order to build two hierarchical structures: one containing clusters of technology classes and the other clusters of science classes. The two data structures are then merged into a single (pseudo-)colored linkage map, colored by the co-occurrences of patents and the scientific publications referred in them. Various zooming facilities are available for visual inspection. After the linkage map for a particular time period is developed, data from other consecutive time periods can be projected onto the map in order to monitor the evolution of the interactions.