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 International Patent Classification system, we compare 12 representative distance measures, which quantify inter-field 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 co-reference and inventor diversification likelihood measures are the best representatives.
A Novel Patent Similarity Measurement Methodology: Semantic Distance and Technological Distance
University of Auckland（奥克兰大学）；University of Arizona（亚利桑那大学）；SR Universe（SR宇宙公司）；Royal Melbourne Institute of Technology（皇家墨尔本理工大学）；Yonsei University（延世大学）
University of Perugia（佩鲁贾大学）；University of Urbino Carlo Bo（乌尔比诺卡罗·博大学）；National Institute of Economic and Social Research（国家经济与社会研究所）；The Productivity Institute（生产力研究所）；Lund University（隆德大学）
