  Metadata quality is crucial for digital objects to be discovered through
digital library interfaces. However, due to various reasons, the metadata of
digital objects often exhibits incomplete, inconsistent, and incorrect values.
We investigate methods to automatically detect, correct, and canonicalize
scholarly metadata, using seven key fields of electronic theses and
dissertations (ETDs) as a case study. We propose MetaEnhance, a framework that
utilizes state-of-the-art artificial intelligence methods to improve the
quality of these fields. To evaluate MetaEnhance, we compiled a metadata
quality evaluation benchmark containing 500 ETDs, by combining subsets sampled
using multiple criteria. We tested MetaEnhance on this benchmark and found that
the proposed methods achieved nearly perfect F1-scores in detecting errors and
F1-scores in correcting errors ranging from 0.85 to 1.00 for five of seven
fields.
