SyGAR – A synthetic data generator for evaluating name disambiguation methods.

Abstract

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 limitation s by proposing a synthetic generator of ambiguous authors hip records called SyGAR . The generator was validated against a gold standard collection of d is ambiguated records , and aplied to evaluate three d is ambiguation method s in a relevant scenario.