Author name disambiguation using a graph model with node splitting and merging based on bibliographic information

• Published in: Scientometrics Vol. 100; no. 1; pp. 15 - 50
• Main Authors: Shin, Dongwook, Kim, Taehwan, Choi, Joongmin, Kim, Jungsun
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.07.2014; Springer
• Subjects: Authority control; Authors; Bibliometrics. Scientometrics; Bibliometrics. Scientometrics. Evaluation; Co authorship; Computer Science; Digital libraries; Exact sciences and technology; Information and communication sciences; Information science. Documentation; Information Storage and Retrieval; Library and information science. General aspects; Library Science; Names; Sciences and techniques of general use; Heteronymous name resolution; Namesake resolution; Graph model; Digital library; Author name disambiguation; Scientific research; Classification; Metric; Coauthorship analysis; Bibliographic data
• ISSN: 0138-9130; 1588-2861
• DOI: 10.1007/s11192-014-1289-4

Author ambiguity mainly arises when several different authors express their names in the same way, generally known as the namesake problem, and also when the name of an author is expressed in many different ways, referred to as the heteronymous name problem. These author ambiguity problems have long been an obstacle to efficient information retrieval in digital libraries, causing incorrect identification of authors and impeding correct classification of their publications. It is a nontrivial task to distinguish those authors, especially when there is very limited information about them. In this paper, we propose a graph based approach to author name disambiguation, where a graph model is constructed using the co-author relations, and author ambiguity is resolved by graph operations such as vertex (or node) splitting and merging based on the co-authorship. In our framework, called a G raph F ramework for A uthor D isambiguation (GFAD), the namesake problem is solved by splitting an author vertex involved in multiple cycles of co-authorship, and the heteronymous name problem is handled by merging multiple author vertices having similar names if those vertices are connected to a common vertex. Experiments were carried out with the real DBLP and Arnetminer collections and the performance of GFAD is compared with three representative unsupervised author name disambiguation systems. We confirm that GFAD shows better overall performance from the perspective of representative evaluation metrics. An additional contribution is that we released the refined DBLP collection to the public to facilitate organizing a performance benchmark for future systems on author disambiguation.