Instant prediction of scientific paper cited potential based on semantic and metadata features: Taking artificial intelligence field as an example.

With the continuous increase in the number of academic researchers, the volume of scientific papers is also increasing rapidly. The challenge of identifying papers with greater potential academic impact from this large pool has received increasing attention. The citation frequency of a paper is often used as an objective indicator to gauge the academic influence of the paper. The task of citation frequency prediction based on historical citation data in previous studies can achieve high accuracy. However, it can only be executed after the paper has been published for a period. The delay is not conducive to timely discovery of papers with high citation frequency. In this paper, we propose a novel method for predicting cited potential of a paper based on the metadata and semantic information, which can predict the cited potential of academic paper instantly once it has been published. Specifically, the semantic information, such as abstract, semantic span and semantic inflection, is extracted to enhance the ability of the prediction model based on machine learning. To prove the effectiveness and rationality of cited potential prediction model, we conduct two experiments to validate the model and find the most effective combination of input information. The empirical experiments show that the prediction accuracy of our proposed model can reach 88% for the instant prediction of citation.