3260 papers • 126 benchmarks • 313 datasets
This task consists of the automatic assessment of the quality of the metadata of an information system.
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This work investigates methods to automatically detect, correct, and canonicalize scholarly metadata, using seven key fields of electronic theses and dissertations (ETDs) as a case study to propose MetaEnhance, a framework that utilizes state-of-the-art artificial intelligence methods to improve the quality of these fields.
A framework to automatically determine the quality of open data catalogs, addressing the need for efficient and reliable quality assessment mechanisms and to empower data-driven organizations to make informed decisions based on trustworthy and well-curated data assets is proposed.
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