Research on Joint Representation Learning Methods for Entity Neighborhood Information and Description Information
This work addresses embedding issues in a domain-specific knowledge graph for programming education, representing an incremental improvement.
The authors tackled poor embedding performance in a programming design course knowledge graph by proposing a joint representation learning model that combines entity neighborhood and description information, achieving favorable performance and outperforming baseline models.
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.