Author Name Disambiguation based on Capsule Network via Semantic and Structural Features.

Author Name Disambiguation (AND) is a crucial task in the knowledge engineering of the bibliography. In academic search systems, author name ambiguity is a common phenomenon caused by different authors with the same name and leads to that author name can not be used to reliably identify all scholar authors. In recent researches, one papers’ attributes are often used to learn its representation as feature. However, most existing methods ignore to extract deep features and potential relationship among papers. To address the problem, we propose a novel model named Author Name Disambiguation based on Capsule Network via Semantic and Structural Features (ADSSF). ADSSF uses both supervised and unsupervised methods to learn the representation of papers. First, we present a new Capsule-Networks-based feature extraction model which can mine deep features and potential relationship. And then, in the representation learning of papers, ADFFS fuses the semantic and structural features of papers by multi-task learning. Finally, a clustering method is leveraged to correctly cluster authors. Experimental results on the AMiner datasets demonstrate that the ADSSF outperforms the state-of-the-art baselines.