Context Aware Recommendation Engine for Metadata Submission

While good metadata is essential in finding, interpreting, and reusing data, the authoring of metadata is considered highly tedious and often incomplete. It is imperative to make the authoring of metadata a manageable task. To-wards easing the burden of authoring high quality metadata, we have developed a data-driven framework that learns as-sociation between data elements to suggest context-sensitive metadata values. We demonstrate our framework in the con-text of microarray annotations from the Gene Expression Omnibus (GEO).