A Hybrid Machine Learning Method to Author Name Disambiguation

Abstract

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Digital bibliographic repositories, including publications, authors, and research fields are essential for sharing scientific information. Nevertheless, the information retrieval, extraction, and classification efficiency in such archives is threatened by author name ambiguity. This paper addresses the Author Name Disambiguation (AND) problem by proposing a hybrid machine learning method integrating Bidirectional Encoder Representations from Transformers (BERT), Graph Convolutional Network (GCN), and Graph Enhanced Hierarchical Agglomerative Clustering (GHAC) approaches. The BERT model extracts textual data from scientific documents, the GCN structures global data from academic graphs, and GHAC considers heterogeneous networks’ global context to identify scientific collaboration patterns. We compare the hybrid method with AND state-of-the-art work using a publicly accessible data set consisting of 7,886 documents, 137 unique authors, and 14 groups of ambiguous authors, along with recognized validation metrics. The results achieved a high precision score of 93.8%, recall of 96.3%, F1-measure of 95%, Average Cluster Purity (ACP) of 96.5%, Average Author Purity (AAP) of 97.4% and K-Metric of 96.9%. Compared to the AND baseline approach, the hybrid method presents better results indicating a promising approach.