Hierarchical main path analysis to identify decompositional multi-knowledge trajectories

Abstract

Purpose The purpose of this paper is to propose a quantitative method for identifying multiple and hierarchical knowledge trajectories within a specific technological domain (TD). Design/methodology/approach The proposed method as a patent-based data-driven approach is basically based on patent classification systems and patent citation information. Specifically, the method first analyzes hierarchical structure under a specific TD based on patent co-classification and hierarchical relationships between patent classifications. Then, main paths for each sub-TD and overall-TD are generated by knowledge persistence-based main path approach. The all generated main paths at different level are integrated into the hierarchical main paths. Findings This paper conducted an empirical analysis by using Genome sequencing technology. The results show that the proposed method automatically identifies three sub-TDs, which are major functionalities in the TD, and generates the hierarchical main paths. The generated main paths show knowledge flows across different sub-TDs and the changing trends in dominant sub-TD over time. Originality/value To the best of the authors’ knowledge, the proposed method is the first attempt to automatically generate multiple hierarchical main paths using patent data. The generated main paths objectively show not only knowledge trajectories for each sub-TD but also interactive knowledge flows among sub-TDs. Therefore, the method is definitely helpful to reduce manual work for TD decomposition and useful to understand major trajectories for TD.

Developmental Trajectories in Blockchain Technology Using Patent-Based Knowledge Network Analysis

“…Third, traversal counts based search path from the starting nodes has high possibility to omit important patents and knowledge flows [5]. Recent research in complex networks has greatly improved the understanding in structure and complexity of knowledge network and introduced KPbased main path analysis [5,13,14]. KP is a metric to measure how much knowledge in a patent is inherited to the recent developments in a knowledge network [30,31] and so KP can quantify patent's technological value from the global citation perspective.…”

“…Then, the backward and forward searching from the identified high KP patents identifies main paths. Since the mechanism of the backward and forward searching is to select patents having the highest value of global persistence among the directly linked patents on the citation network, main paths from starting patents to endpoint patents can be identified [13].…”

“…We used a patent database and analyzed a patent citation network. Patents are one of the most reliable sources for technological knowledge [1][2][3] and have been widely used for identification of recent trends of high technologies discovery of new technology opportunity and development of technology roadmaps and plans [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. Since patent citations represent knowledge flows or inheritance between the cited patent and the citing patent, a patent citation network was constructed to visualize all knowledge flows within the blockchain technology.…”

Investigating Company’s Technical Development Directions Based on Internal Knowledge Inheritance and Inventor Capabilities: The Case of Samsung Electronics

“…Specifically, this paper combined the main path analysis and inventor network analysis to identify a firm's internal technical trajectories and predict its future development directions. A main path analysis has been widely used for understanding technical changes [9][10][11][12][13][14] and trajectories under a technical field [15][16][17][18][19][20][21][22][23][24][25][26][27]. This approach identifies the major knowledge flows within a knowledge network by minimizing the network complexity, and so it can show the major knowledge flows within a company.…”

“…In addition, the last nodes in a main path can be the specific clues to predict future development directions [28]. Given that the technical capabilities of a company cannot be evolved in short time, but accumulated and inherited over time through continuous R&D activity, we adopted the knowledge persistence (KP)-based main path analysis, which can quantify how much knowledge of a patent was inherited to later inventions [23,29]. A co-inventor network analysis assesses the inventors' impact or power in a co-inventor network and finds key inventors who make a huge influence on the internal technical development [30][31][32][33].…”