Two supervised learning approaches for name disambiguation in author citations
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Name disambiguation in author citations using a K-way spectral clustering method
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Comparative study of name disambiguation problem using a scalable blocking-based framework
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Efficient topic-based unsupervised name disambiguation
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Efficient name disambiguation for large-scale databases
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
A tool for generating synthetic authorship records for evaluating author name disambiguation methods
Information Sciences: an International Journal
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Name ambiguity in the context of bibliographic citations is one of the hardest problems currently faced by the digital library community. Several methods have been proposed in the literature, but none of them provides the perfect solution for the problem. More importantly, basically all of these methods were tested in limited and restricted scenarios, which raises concerns about their practical applicability. In this work, we deal with these limitations by proposing a synthetic generator of ambiguous authorship records called SyGAR. The generator was validated against a gold standard collection of disambiguated records, and applied to evaluate three disambiguation methods in a relevant scenario.