Measuring technological distance for patent mapping
比较12种专利分类距离测量方法，通过分析专利网络图的结构特性，选出最优方法用于构建技术领域图谱，帮助竞争情报和创新决策。
Recent works in the information science literature have presented cases of using patent databases and patent classification information to construct network maps of technology fields, which aim to aid in competitive intelligence analysis and innovation decision making. Constructing such a patent network requires a proper measure of the distance between different classes of patents in the patent classification systems. Despite the existence of various distance measures in the literature, it is unclear how to consistently assess and compare them, and which ones to select for constructing patent technology network maps. This ambiguity has limited the development and applications of such technology maps. Herein, we propose to compare alternative distance measures and identify the superior ones by analyzing the differences and similarities in the structural properties of resulting patent network maps. Using United States patent data from 1976 to 2006 and the International Patent Classification (IPC) system, we compare 12 representative distance measures, which quantify interfield knowledge base proximity, field‐crossing diversification likelihood or frequency of innovation agents, and co‐occurrences of patent classes in the same patents. Our comparative analyses suggest the patent technology network maps based on normalized coreference and inventor diversification likelihood measures are the best representatives.