Abstract
Individuals often appear with multiple names when considering large bibliographic datasets, giving rise to the synonym ambiguity problem. Although most related works focus on resolving name ambiguities, this work focus on classifying and characterizing multiple name usage patterns—the root cause for such ambiguity. By considering real examples bibliographic datasets, we identify and classify patterns of multiple name usage by individuals, which can be interpreted as name change, rare name usage, and name co-appearance. In particular, we propose a methodology to classify name usage patterns through a supervised classification task and show that different classes are robust (across datasets) and exhibit significantly different properties. We show that the collaboration network structure emerging around nodes corresponding to ambiguous names from different name usage patterns have strikingly different characteristics, such as their common neighborhood and degree evolution. We believe such differences in network structure and in name usage patterns can be leveraged to design more efficient name disambiguation algorithms that target the synonym problem.
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Notes
Link to download the XML of the entire DBLP database: http://dblp.uni-trier.de/xml/.
See details on DBLP’s handling of synonyms at http://dblp.uni-trier.de/faq/How+does+dblp+handle+homonyms+and+synonyms.html.
CNPq is the Brazilian National Research Council responsible for funding research, similar to the National Science Foundation (NSF) in the United States of America.
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This research received financial support through grants from FAPERJ and CNPq (Brazil).
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Gomide, J., Kling, H. & Figueiredo, D. Name usage pattern in the synonym ambiguity problem in bibliographic data. Scientometrics 112, 747–766 (2017). https://doi.org/10.1007/s11192-017-2410-2
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DOI: https://doi.org/10.1007/s11192-017-2410-2