Discovering knowledge combinations in multidimensional collaboration network: A method based on trust link prediction and knowledge similarity

摘要

Discovering knowledge combination has been considered an effective strategy for knowledge retrieval and knowledge discovery. Generally, knowledge combination driven by close-cooperation can be achieved via modeling the process of knowledge transfer. However, the existing studies seldom built connections between knowledge transfer and the identification of knowledge combination, especially in the existing knowledge transfer models, less attention is paid to the effects of trust and knowledge similarity. Therefore, the research motivations of this paper are to model the process of knowledge transfer and to further discover knowledge combinations. To minimize the risks of knowledge transfer, both the knowledge similarity and the trust embodied within need to be taken into account, thereby proposing a bi-layered network regarding knowledge similarity and trust. First, a trust network is obtained novelly whereby the proposed method of trust link prediction. Accordingly, a directed knowledge flow network is constructed through a proposed knowledge transfer model endowed with trust scores. Second, knowledge combinations in a knowledge flow network are therefore acquired by adopting a community detection method. Third, various probabilities of knowledge combinations based on the maximum network modularity are calculated with respect to the influence of knowledge similarity on cooperation probability. The key contributions of this paper are summarized as an effective approach to identifying knowledge combinations conducted to improve the efficiencies of knowledge management. Related experiments and comparisons are presented to illustrate the practicalities of the proposed method. (C) 2020 Elsevier B.V. All rights reserved.