Framework for Author Name Disambiguation in Scientific Papers Using an Ontological Approach and Deep Learning

Abstract

The aim of this paper is to solve the problem of disambiguation of authors’ names in scientific papers. In particular, it focuses on the problem of synonyms and homonyms. Thus, we often find two or more names written in different forms denoting the same person. Moreover, there may be several authors using the same name. To address both the synonym and homonym problems in scientific papers, we propose a framework that uses a hybrid approach of an ontological model and a deep learning model. First, we describe the design of the ontology model, the automatic ontology creation process, and the construction of a weighted co-author network through a set of semantic rules and queries. Second, the selected features are preprocessed during the attribute engineering process to measure the similarity indicator for each feature. Third, the similarity indicators are reduced to a vector space model and used as input to the Deep Learning-based author name disambiguation method to model different types of features. Fourth, the proposed framework is tested on smaller groups of the gold standard large dataset of scientific papers from several international databases named LAGOSAND and achieves promising results compared to other similar solutions proposed in the literature.