Towards Automatic Evaluation of Metadata Quality in Digital Repositories

Thanks to recent developments on automatic generation of metadata and interoperability between repositories, the production, management and consumption of metadata is vastly surpassing the human capacity to review or process this information. However, we need to assure that low quality metadata does not compromise the performance of the services that the repository provides to its users. We contend there is a need for automatic assessment of the quality of metadata in digital repositories, so tools or users can be alerted about low quality records. In this paper, we present several quality metrics for metadata based on quality evaluation frameworks used for human quality review. We applied these metrics to a sample of records from a real repository and compared the results with the quality assessment given to the same records by a group of human reviewers. Through correlation and regression analysis, we found that one of the metrics, the text information content, could be used as a predictor of the human evaluation. While these metrics are not proposed as a definitive measurement of the complete multi-dimensional quality of the metadata record, we present ways in which they can be used to enhance the functionality of digital repositories.