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To address the issue of poor embedding performance in the knowledge graph ofa programming design course, a joint represen-tation learning model thatcombines entity neighborhood infor-mation and description information isproposed. Firstly, a graph at-tention network is employed to obtain thefeatures of entity neigh-boring nodes, incorporating relationship features toenrich the structural information. Next, the BERT-WWM model is utilized inconjunction with attention mechanisms to obtain the representation of entitydescription information. Finally, the final entity vector representation isobtained by combining the vector representations of entity neighborhoodinformation and description information. Experimental results demonstrate thatthe proposed model achieves favorable performance on the knowledge graphdataset of the pro-gramming design course, outperforming other baseline models.

Research on Joint Representation Learning Methods for Entity Neighborhood Information and Description Information