+ Various types of knowledge can be classified into different domains based on their semantics for better knowledge management. − Only the co-occurrence relationship among knowledge was considered. The weights of knowledge were neglected.
+ Constructed a semantic expression model of dynamic knowledge network in the context of big data. − Only considered the semantic relationship among knowledge. No attention to the relation between user and knowledge and product for product innovation.
+ The importance of each knowledge and the closeness among each knowledge can be clearly indicated in the network. − Some character weights of knowledge and the similarity relationship among knowledge were neglected.
+ Comprehensively considering the users, attention, and frequency of knowledge, the authors constructed user network, text network, and semantics network of knowledge and the relationship among them. − Some character weights of knowledge and the similarity relationship among knowledge were neglected.
+ Support for knowledge acquisition and integration in the process of innovation and the exploration and evaluation of innovation issues. − No attention to the relation between user and knowledge.
+ Constructed a knowledge network model based on data acquisition, transformation, and utilization to support product development and design. − No attention to the relation between user and knowledge.
This paper
✓
✓
✓
+ (1) Combined some of the advantages of the construction of co-creation user and knowledge network in the related literature. (2) Further considered some important but neglected weights and relationships of users and knowledge to make the network more comprehensive. (3) Extend the previous network to a more systematic “user-knowledge-product” co-creation cyberspace model for better product innovation. − The comparative analysis with the related research is required in the future to prove the priority of the model.