  Author name ambiguity in a digital library may affect the findings of
research that mines authorship data of the library. This study evaluates author
name disambiguation in DBLP, a widely used but insufficiently evaluated digital
library for its disambiguation performance. In doing so, this study takes a
triangulation approach that author name disambiguation for a digital library
can be better evaluated when its performance is assessed on multiple labeled
datasets with comparison to baselines. Tested on three types of labeled data
containing 5,000 ~ 700K disambiguated names and 6M pairs of disambiguated
names, DBLP is shown to assign author names quite accurately to distinct
authors, resulting in pairwise precision, recall, and F1 measures around 0.90
or above overall. DBLP's author name disambiguation performs well even on large
ambiguous name blocks but deficiently on distinguishing authors with the same
names. When compared to other disambiguation algorithms, DBLP's disambiguation
performance is quite competitive, possibly due to its hybrid disambiguation
approach combining algorithmic disambiguation and manual error correction. A
discussion follows on strengths and weaknesses of labeled datasets used in this
study for future efforts to evaluate author name disambiguation on a digital
library scale.
