Research on Joint Representation Learning Methods for Entity
  Neighborhood Information and Description Information
Le Xiao Xin Shan Yuhua Wang Miaolei Deng
To address the issue of poor embedding performance in the knowledge graph of
a programming design course, a joint represen-tation learning model that
combines entity neighborhood infor-mation and description information is
proposed. Firstly, a graph at-tention network is employed to obtain the
features of entity neigh-boring nodes, incorporating relationship features to
enrich the structural information. Next, the BERT-WWM model is utilized in
conjunction with attention mechanisms to obtain the representation of entity
description information. Finally, the final entity vector representation is
obtained by combining the vector representations of entity neighborhood
information and description information. Experimental results demonstrate that
the proposed model achieves favorable performance on the knowledge graph
dataset of the pro-gramming design course, outperforming other baseline models.