Ordered and quantum treemaps: Making effective use of 2D space to display hierarchies
ACM Transactions on Graphics (TOG)
An information-theoretic perspective of tf—idf measures
Information Processing and Management: an International Journal
Automating metadata generation: the simple indexing interface
WWW '05 Proceedings of the 14th international conference on World Wide Web
DCMI '03 Proceedings of the 2003 international conference on Dublin Core and metadata applications: supporting communities of discourse and practice---metadata research & applications
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
An infrastructure for acquiring high quality semantic metadata
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Repurposing learning object components
OTM'05 Proceedings of the 2005 OTM Confederated international conference on On the Move to Meaningful Internet Systems
A lightweight metadata quality tool
Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries
Metrics for metadata quality assurance and their implications for digital libraries
ICADL'11 Proceedings of the 13th international conference on Asia-pacific digital libraries: for cultural heritage, knowledge dissemination, and future creation
Comparing metadata quality in the Europeana context
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
Information Systems and e-Business Management
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Thanks to recent developments on automatic generation of metadata and interoperability between repositories, the production, management and consumption of learning object metadata is vastly surpassing the human capacity to review or process these metadata. However, we need to make sure that the presence of some low quality metadata does not compromise the performance of services that rely on that information. Consequently, there is a need for automatic assessment of the quality of metadata, so that tools or users can be alerted about low quality instances. In this paper, we present several quality metrics for learning object metadata. 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 this metric is not a definitive measurement of the “real” quality of the metadata record, we present several ways in which it can be used. We also propose new research in other quality dimensions of the learning object metadata.