基于深度学习的创新主题智能挖掘算法研究

[Objective] This paper aims to identify innovative topics from massive volumes of texts. [Methods] First, we extracted knowledge points with heavier weights from the data of scholarly knowledge graph. Then, these knowledge points were labeled as innovative seeds from the perspectives of “popularity”, “novelty” and “authority”. Third, we computed the knowledge correlation of the innovative seeds. Finally, the results were input to a deep learning model trained by large amounts of sci-tech papers to generate innovative topics. Note: the model is sequence to sequence with Bi-LSTM. [Results] We used Chinese research papers on artificial intelligence as the experimental data and found the average innovation score of the retrieved topics was 6.52, which were evaluated by experts manually. [Limitations] At present, contents of the knowledge graph and the training datasets need to be improved. [Conclusions] The proposed model, which identifies innovative topics from scholarly papers, could be optimized in the future.