Exploiting Higher Order Multi-dimensional Relationships with Self-attention for Author Name Disambiguation

Abstract

Name ambiguity is a prevalent problem in scholarly publications due to the unprecedented growth of digital libraries and number of researchers. An author is identified by their name in the absence of a unique identifier. The documents of an author are mistakenly assigned due to underlying ambiguity, which may lead to an improper assessment of the author. Various efforts have been made in the literature to solve the name disambiguation problem with supervised and unsupervised approaches. The unsupervised approaches for author name disambiguation are preferred due to the availability of a large amount of unlabeled data. Bibliographic data contain heterogeneous features, thus recently, representation learning-based techniques have been used in literature to embed heterogeneous features in common space. Documents of a scholar are connected by multiple relations. Recently, research has shifted from a single homogeneous relation to multi-dimensional (heterogeneous) relations for the latent representation of document. Connections in graphs are sparse, and higher order links between documents give an additional clue. Therefore, we have used multiple neighborhoods in different relation types in heterogeneous graph for representation of documents. However, different order neighborhood in each relation type has different importance which we have empirically validated also. Therefore, to properly utilize the different neighborhoods in relation type and importance of each relation type in the heterogeneous graph, we propose attention-based multi-dimensional multi-hop neighborhood-based graph convolution network for embedding that uses the two levels of an attention, namely, (i) relation level and (ii) neighborhood level, in each relation. A significant improvement over existing state-of-the-art methods in terms of various evaluation matrices has been obtained by the proposed approach.