CRM-SBKG: Effective Citation Recommendation by Siamese BERT and Knowledge Graph

Citation recommendation focuses on recommending references to the given document automatically. While the existing works have achieved improvements in citation recommendation, their quality may still be degraded due to the cold-start problem or insufficient use of features. To alleviate these problems, we investigate the possibility of fully utilizing a document's features from different dimensions. Specifically, we present a citation recommendation model based on Siamese BERT and Knowledge Graph (CRM-SBKG), a mixture model that mines the text feature and entity feature to obtain the recommendation list. We use the BERT model with the Siamese network for text feature extraction and build a citation knowledge graph where entity feature is extracted. We apply the proposed model on Open Academic Graph (OAG) and DataBase systems and Logic Programming (DBLP). The results show that our model outperforms the baselines significantly. It reflects that the proposed model can extract features more effectively and make better citation recommendations.