The Indicator System Of Patent Knowmetrics And Application

Patent is a kind of knowledge carrier concentrating on technological information, economic information, business information and other forms of information. Especially, as the title, abstract and claim of the patent text, which could be analyzed to reveal its technical knowledge. With the development of science and technology of China, knowledge-based quantitative analysis on volumes of patent texts is able to demonstrate the knowledge construction and development status of patent technology of our country. Based on the particular patent structure in the SIPOD, the paper aims to find the proper metrics method and indicator to calculate the patent knowledge.First of all, based on the fundamental theory and relevant concept of knowledge and knowmetrics, the paper defines the patent knowmetrics with patent knowledge’s features established in inventiveness, systematicness, and networking. Metric object of patent knowmetrics is explained based on the knowledge unit, because kinds of knowledge units interacting together assemble the stratiform cluster knowledge link, knowledge group and knowledge network. Therefore, a construction of indicator system has been established with a four level structure of patent knowledge unit, knowledge link, knowledge group and knowledge network. In the perspective of patent knowledge unit, the frequency, weight, degree and betweenness are applied to measure its quantity and quality. On the respect of knowledge link, the indicators frequency, length, impact and betweenness are formed to analyze its structure and function. In the field of patent knowledge group, the indicator strength, size and cluster are used to make comparison among a large of groups. Furthermore, the size, distance and density are adopted to describe the feature of patent knowledge network.Secondly, the paper designs different methods to extract different patent knowledge in different levels, based on the unique patent structure in the SIPOD. In the level of knowledge unit, the algorithm of maximum string matching is applied, combining with the position of knowledge unit, and then cloud of knowledge unit is applied to knowledge visualization. In this research, patent knowledge link has been extracted with frequency item set of Apriori mining algorithms in association rules. Taking example by identification methods of complex network analysis, patent knowledge group has been recognized and calculated with Lambda Set from social network analysis. With regard to knowledge network, the analysis method in the complex network is used to show its characteristics.Finally, taking the76,877patents in the H04L field in the SIPOD as the example, closely connecting with the next generation mobile communication and tri-networks integration, knowmetrics analysis is carried out with the indicator system built above. With the results shown in the four levels, the knowledge structure of H04L filed has been revealed from the perspective of knowledge unit, knowledge link, knowledge group and knowledge network. At the same time, the research hotspots in the field has been identified with the indicator frequency and the length in the patent knowledge link, while the indicator cluster in the knowledge group is used to represent the core technological group knowledge. Simultaneously, the core technological knowledge and the key technological knowledge have been positioned.