Real-time name disambiguation aims to accurately associate new papers to the correct author among same-name candidates in real-time.This paper proposes a subgraph-enhanced real-time name disambiguation model,RND-all,which uses the structural features between the disambiguation paper and the candidate authors to improve the accuracy.In this model,we construct subgraphs based on the attributes of the paper to be disambiguated and the profiles of the candidate authors with the same name,respectively.Then a subgraph structure feature extraction framework is established to calculate graph-correlation features.Finally,the ensemble learning is applied to in-tegrate the structural information and the semantic information,which are derived by feature engineering and se-mantic text embedding.Experimental results show that incorporating structural information can effectively improve the accuracy of real-time name disambiguation tasks,and RND-all ranks first on the test set of million-level name disambiguation benchmark WhoIsWho.
real-time name disambiguationgraph neural networkstructural informationensemble learning
NETL
NSTL
万方数据
