Abstract
To address the issue of poor embedding performance in the knowledge graph of a programming design course, a joint representation learning model that combines entity neighborhood information and description information is proposed. Firstly, a graph attention network is employed to obtain the features of entity neighboring 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 programming design course, outperforming other baseline models.
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Xiao, L., Shan, X., Wang, Y., Deng, M. (2023). Research on Joint Representation Learning Methods for Entity Neighborhood Information and Description Information. In: Wang, H., Han, X., Liu, M., Cheng, G., Liu, Y., Zhang, N. (eds) Knowledge Graph and Semantic Computing: Knowledge Graph Empowers Artificial General Intelligence. CCKS 2023. Communications in Computer and Information Science, vol 1923. Springer, Singapore. https://doi.org/10.1007/978-981-99-7224-1_4
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DOI: https://doi.org/10.1007/978-981-99-7224-1_4
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