  Name ambiguity is common in academic digital libraries, such as multiple
authors having the same name. This creates challenges for academic data
management and analysis, thus name disambiguation becomes necessary. The
procedure of name disambiguation is to divide publications with the same name
into different groups, each group belonging to a unique author. A large amount
of attribute information in publications makes traditional methods fall into
the quagmire of feature selection. These methods always select attributes
artificially and equally, which usually causes a negative impact on accuracy.
The proposed method is mainly based on representation learning for
heterogeneous networks and clustering and exploits the self-attention
technology to solve the problem. The presentation of publications is a
synthesis of structural and semantic representations. The structural
representation is obtained by meta-path-based sampling and a skip-gram-based
embedding method, and meta-path level attention is introduced to automatically
learn the weight of each feature. The semantic representation is generated
using NLP tools. Our proposal performs better in terms of name disambiguation
accuracy compared with baselines and the ablation experiments demonstrate the
improvement by feature selection and the meta-path level attention in our
method. The experimental results show the superiority of our new method for
capturing the most attributes from publications and reducing the impact of
redundant information.
