MetaEnhance: Metadata Quality Improvement for Electronic Theses and Dissertations of University Libraries
This addresses metadata quality issues for university libraries, enabling better discovery of digital objects, but it is incremental as it applies existing AI methods to a specific domain.
The paper tackled the problem of poor metadata quality in electronic theses and dissertations by proposing MetaEnhance, a framework that uses AI methods to automatically detect and correct errors, achieving F1-scores up to 1.00 for error detection and 0.85 to 1.00 for correction in five of seven fields.
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.