  Metadata-the machine-readable descriptions of the data-are increasingly seen
as crucial for describing the vast array of biomedical datasets that are
currently being deposited in public repositories. While most public
repositories have firm requirements that metadata must accompany submitted
datasets, the quality of those metadata is generally very poor. A key problem
is that the typical metadata acquisition process is onerous and time consuming,
with little interactive guidance or assistance provided to users. Secondary
problems include the lack of validation and sparse use of standardized terms or
ontologies when authoring metadata. There is a pressing need for improvements
to the metadata acquisition process that will help users to enter metadata
quickly and accurately. In this paper we outline a recommendation system for
metadata that aims to address this challenge. Our approach uses association
rule mining to uncover hidden associations among metadata values and to
represent them in the form of association rules. These rules are then used to
present users with real-time recommendations when authoring metadata. The
novelties of our method are that it is able to combine analyses of metadata
from multiple repositories when generating recommendations and can enhance
those recommendations by aligning them with ontology terms. We implemented our
approach as a service integrated into the CEDAR Workbench metadata authoring
platform, and evaluated it using metadata from two public biomedical
repositories: US-based National Center for Biotechnology Information (NCBI)
BioSample and European Bioinformatics Institute (EBI) BioSamples. The results
show that our approach is able to use analyses of previous entered metadata
coupled with ontology-based mappings to present users with accurate
recommendations when authoring metadata.
