Abstract
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 Graph Framework for Author Disambiguation (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.
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Notes
By a citation record, we mean a set of bibliographic attributes containing author names, paper title, and publication venue of a particular publication.
GFAD also relies on paper title in addition to co-authorship, but it is only used at outlier removal step, if necessary, to meet the specific objectives of the system.
We can have isolated vertices during the graph construction process and/or after namesake resolution process.
To maximize the possibility of selecting different name variations denoting the same person, while minimizing the chance of judging similar names denoting different person as the same person, suitable threshold values must be manually determined in the first place. So we empirically determined the threshold value after experimenting with randomly collected 200 name pairs including 100 pairs of name variations and 100 pairs of similar names.
To measure and analyze the ratios of the occurrence frequencies of three failure cases, we randomly selected 18 ambiguous groups from the Arnetminer collection.
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Shin, D., Kim, T., Choi, J. et al. Author name disambiguation using a graph model with node splitting and merging based on bibliographic information. Scientometrics 100, 15–50 (2014). https://doi.org/10.1007/s11192-014-1289-4
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DOI: https://doi.org/10.1007/s11192-014-1289-4