Presentation paper for the 2017 International Conference on Knowledge Management. This paper introduces the study and practice of design knowledge management and knowledge transfer across the design of a newly built nuclear power plant in China.

WeChat group-based online medical community (WGMC) is increasingly accepted by the public because of its high efficiency, convenience, and shared advantages in seeking medical resources. However, the problem to separate a complete dialogue relationship from the chat records is arising because efficient online community management is based on clear dialogue relationships and clear topics. To solve the problem, we proposed a hybrid three-stages BERT method to recognize the dialogue relationships in "Home of Love" --- a central nervous system tumor online healthy community WeChat group. First, based on the social support theory, a multi-layer BiLSTM model is proposed to classify the conversation scenes into five classes. Then, two domain adaptation methods for transfer learning are designed to optimize the BERT pre-training model for specific tasks using the "Haodaifu" as the training corpus. Finally, a hybrid BERT method based on the text scene information and the pre-training model is proposed to recognize the dialogue relationships, and its feasibility is verified by manual labeling. The results show that adding more prior knowledge to the dialogue recognition model by extracting the social support scene information can effectively improve the classification ability and stability of the model.