Science and technology interactions discovered with a new topographic map-based visualization tool

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. Finally, in order to quantify the dynamics of the interaction, structural equation modeling, a causal modeling technique, is applied to the most significant co-occurrences in the linkage map.