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

TL;DR

This paper proposes a joint representation learning model that combines entity neighborhood and description information using graph attention networks and BERT-WWM, improving embedding performance in a programming course knowledge graph.

Contribution

It introduces a novel model integrating neighborhood and description data for entity embedding, leveraging graph attention and BERT-WWM techniques.

Findings

Model outperforms baseline methods on programming course knowledge graph

Combining neighborhood and description info enhances embedding quality

Utilizes graph attention and BERT-WWM for richer feature extraction

Abstract

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.