A visualization tool of patent topic evolution using a growing cell structure neural network
This research used a cell structure map to visualize technological evolution and showed the developmental trend in a technological field. The basic concept was to organize patents into a map produced by growing cell structures. The map was then disassembled into clusters with similar contexts using the Girvan–Newman algorithm. Next, the continuity between clusters in two snapshots was identified and used as the base for establishing a trajectory in the technology. An analysis of patents in the flaw detection field found that the field was composed of several technological trajectories. Among them, ultrasonic flaw detection, wafer inspection and substrate inspection were relatively larger and more continuing technologies, while infrared thermography defect inspection has been an emerging topic in recent years. It is to be hoped that the map of technology constructed in this research provides insights into the history of technological evolution and helps explain the transition patterns through changes in cluster continuity. This can serve a reference point by experts who attempt to visualize the mapping of technological development or identify the latest focus of attention.
KeywordsTechnology map Growing cell structures Neural networks Girvan–Newman algorithm Natural language processing
- Chakrabarti, D., Kumar, R., & Tomkins, A. (2006). Evolutionary clustering. In Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining, 2006 (pp. 554–560).Google Scholar
- Dodge, M. (2005). Information maps: Tools for document exploration. CASA Working Paper, No. 94. Retrieved from: http://discovery.ucl.ac.uk/174115/1/paper94.pdf.
- Falkowski, T. (2009). Community analysis in dynamic social networks. Dissertation, University Magdeburg.Google Scholar
- Hariri, N., & Shekofteh, M. (2013). The scientific map of medicine in Iran: Category co-citation analysis. Malaysian Journal of Library and Information Science, 18(2), 79–94.Google Scholar
- Kandylas, V., Upham, S., & Ungar, L. H. (2010). Analyzing knowledge communities using foreground and background clusters. ACM Transactions on Knowledge Discovery from Data, 4(2), art. no. 7.Google Scholar
- Marcus, M. P., Marcinkiewicz, M. A., & Santorini, B. (1993). Building a large annotated corpus of English: The Penn Treebank. Computational linguistics, 19(2), 313–330.Google Scholar
- Wise, J. A., Thomas, J. J., Pennock, K., Lantrip, D., Pottier, M., Schur, A., et al. (1995). Visualizing the non-visual: Spatial analysis and interaction with information from text documents. In IEEE Proceedings of Information Visualization, 1995 (pp. 51–58).Google Scholar