Research on Intelligent Evaluation for the Content Innovation of Academic Papers postprint

Abstract: [Purpose/significance] Innovation is the key factor of academic paper evaluation. Based on the knowledge element theory and machine learning theory and algorithm, this paper studies how to intelligently evaluate the innovation of academic papers from the content of paper.[Method/process] Firstly, we constructed 4 knowledge element ontologies of academic papers including ‘research problem ontology’, ‘theory ontology’, ‘method ontology’ and ‘conclusion ontology’, and proposed the model of innovation evaluation. Secondly, we put forward the rules of knowledge element extraction. Word2vec and naive Bayes were used to classify the innovation of theories and methods of academic papers, and SVM model was used to build the rule base of knowledge element extraction. At last, on the basis of the construction of knowledge Meta base of academic papers, we proposed the basic methods of intelligent evaluation of research questions, theories, methods and conclusions of academic papers. We also constructed the process of intelligent evaluation of innovation of academic papers.[Result/conclusion] The feasibility of the methods is verified by the experiment and could provide the references for the realization of intelligently evaluation of academic paper.