A review expert recommendation model based on knowledge graph embedding

Kaixin Zhao; Qiqing Wang

doi:10.1117/12.2656551

20 October 2022 A review expert recommendation model based on knowledge graph embedding

Kaixin Zhao, Qiqing Wang

Proceedings Volume 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022); 124511X (2022) https://doi.org/10.1117/12.2656551
Event: 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 2022, Chongqing, China

Abstract

In recent years, the explosively growing scientific and technological research achievements of China have led to challenges for the review expert selection. A review expert recommendation model based on a knowledge graph (KG) was proposed. The model took the results of knowledge graph embedding (KGE) and expert-term interaction matrix as inputs. After that, the embedding vectors of TransD and graph attention networks (GAT) were used to recommend review experts based on vector similarity. The results show that, compared with other graph embedding models, the proposed model could better capture the characteristics of the research domain of experts and is more conducive to distinguishing the research directions of different experts, so as to recommend relevant experts for projects or papers.

Citation Download Citation

Kaixin Zhao and Qiqing Wang "A review expert recommendation model based on knowledge graph embedding", Proc. SPIE 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 124511X (20 October 2022); https://doi.org/10.1117/12.2656551

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