Frontiers of Data and Domputing ›› 2022, Vol. 4 ›› Issue (2): 39-49.

doi: 10.11871/jfdc.issn.2096-742X.2022.02.004

• Special Issue: Advanced Intelliget Computing Platform and Application • Previous Articles     Next Articles

Graph Matching Text Classification Based on KG

LAN Ge(),WANG Jinyu(),SUN Yufei*(),ZHANG Yuzhi()   

  1. College of Software, Nankai University, Tianjin 300350, China
  • Received:2022-02-06 Online:2022-04-20 Published:2022-04-30
  • Contact: SUN Yufei;;;


[Objective] In the field of natural language processing (NLP), text classification is a well-developed task that benefits many downstream tasks such as article retrieval, recommendation systems, and question answering. Inspired by the role of knowledge graph (KG) in the field of text reasoning, this article explores the way of utilizing the reasoning capability of KG to support text classification. [Methods] This paper proposes graph matching text classification based on KG. Specifically, this paper constructs the corresponding KG for each class according to the task. The model utilizes the semantics and structure information of these KGs to evaluate the relevance of the text to each class’s KG and then classifies the text by synthesizing the evaluations of all KGs. [Conclusions] In order to prove the effectiveness of our proposed model, this paper builds all KGs of classes in two datasets and conducts experiments on those datasets. The experiment results prove that the proposed model achieves high accuracy under the premise of allowing some data to be rejected and further promotes the application of the method.

Key words: text classification, knowledge graph, graph matching, knowledge graph construction, information extraction