An R&D Partner Recommendation Framework Based on a Knowledge Context Hypernetwork for Engineering Technological Innovation
Jin Xu, Wu Tao, Jiexun Li · IEEE Transactions on Engineering Management · 2023
Engineering construction enterprises often need to partner with R&D institutions and jointly carry out technological innovation activities. Identifying the right R&D partner(s) for the job is key. An accurate selection of R&D partners requires examining the knowledge gained from their past innovations and the corresponding contexts. This article introduces a novel R&D partner recommendation framework (PROKCH) based on a knowledge context hypernetwork to analyze the knowledge contexts of engineering technology innovation for recommending R&D partners. First, in this article, eight dimensions of knowledge contexts are defined, and an automated method to extract them from related documents is proposed. Next, a four-layer hypernetwork model is constructed to represent the extracted knowledge contexts. Finally, we develop a novel algorithm based on node similarity in the hypernetwork for recommending R&D partners. Experiments on a real dataset of recent railway tunneling projects in China demonstrate that the proposed PROKCH framework can improve the performance of R&D institution recommendations.