Heterogeneous deep graph convolutional network with citation relational BERT for COVID-19 inline citation recommendation

Tao Dai; Jie Zhao; Dehong Li; Shun Tian; Xiangmo Zhao; Shirui Pan

doi:10.1016/j.eswa.2022.118841

Heterogeneous deep graph convolutional network with citation relational BERT for COVID-19 inline citation recommendation

Expert Syst Appl. 2023 Mar 1:213:118841. doi: 10.1016/j.eswa.2022.118841. Epub 2022 Sep 17.

Authors

Tao Dai¹, Jie Zhao², Dehong Li², Shun Tian¹, Xiangmo Zhao³, Shirui Pan⁴

Affiliations

¹ School of Future Transportation, Chang'an University, Xi'an, Shaanxi 710064, China.
² School of Economics and Management, Chang'an University, Xi'an, Shaanxi 710064, China.
³ School of Information Engineering, Chang'an University, Xi'an, Shaanxi 710064, China.
⁴ Faculty of Information Technology, Monash University, Melbourne, Australia.

Abstract

The outbreak of COVID-19 brings almost the biggest explosions of scientific literature ever. Facing such volume literature, it is hard for researches to find desired citation when carrying out COVID-19 related research, especially for junior researchers. This paper presents a novel neural network based method, called citation relational BERT with heterogeneous deep graph convolutional network (CRB-HDGCN), for COVID-19 inline citation recommendation task. The CRB-HDGCN contains two main stages. The first stage is to enhance the representation learning of BERT model for COVID-19 inline citation recommendation task through CRB. To achieve the above goal, an augmented citation sentence corpus, which replaces the citation placeholder with the title of the cited papers, is used to lightly retrain BERT model. In addition, we extract three types of sentence pair according citation relation, and establish sentence prediction tasks to further fine-tune the BERT model. The second stage is to learn effective dense vector of nodes among COVID-19 bibliographic graph through HDGCN. The HDGCN contains four layers which are essentially all sub neural networks. The first layer is initial embedding layer which generates initial input vectors with fixed size through CRB and a multilayer perceptron. The second layer is a heterogeneous graph convolutional layer. In this layer, we expand traditional homogeneous graph convolutional network into heterogeneous by subtly adding heterogeneous nodes and relations. The third layer is a deep attention layer. This layer uses trainable project vectors to reweight the node importance simultaneously according to both node types and convolution layers, which further promotes the performance of learnt node vectors. The last decoder layer recovers the graph structure and let the whole network trainable. The recommendation is finally achieved by integrating the high performance heterogeneous vectors learnt from CRB-HDGCN with the query vectors. We conduct experiments on the CORD-19 and LitCovid datasets. The results show that compared with the second best method CO-Search, CRB-HDGCN improves MAP, MRR, P@100 and R@100 with 21.8%, 22.7%, 37.6% and 21.2% on CORD-19, and 29.1%, 25.9%, 15.3% and 11.3% on LitCovid, respectively.

Keywords: COVID-19 citation recommendation; Citation enhanced BERT; Deep graph convolutional network; Heterogeneous graph; Text representation learning.