Heterogeneous Knowledge Learning of Predictive Academic Intelligence in Transportation

The widespread communication of academic ideas and research achievements through literature and media has generated massive academic big data. Analyzing such academic big data and discovering knowledge can discover comprehensive and predictive academic intelligence and provide corresponding services, which are valuable for scholars, journals, institutions and governments for career assessment, topic selection, funding management and resource allocation. This paper proposes a heterogeneous knowledge-learning method for understanding and predicting academic impact in the transportation field. The proposed method is illustrated on the academic big data collected from the papers published in 34 transportation journals from 2008 to 2018. We extract four types of features including bibliometric, altmetric, network and semantic features, and build hybrid feature embedding via TransR and Doc2vec that involving domain knowledge. Further, an academic impact prediction model for articles named as Hy-LSTM-Att is proposed, which weighs the hybrid features by the attention mechanism and predicts academic impact with the bi-LSTM recurrent neural networks. Experimental results demonstrate that the proposed Hy-LSTM-Att model outperforms competing shallow and deep learning models. Additionally, the feature ablation experiments illustrate that the four types of features positively contribute to the performance of impact prediction.