  The metadata about scientific experiments published in online repositories
have been shown to suffer from a high degree of representational
heterogeneity---there are often many ways to represent the same type of
information, such as a geographical location via its latitude and longitude. To
harness the potential that metadata have for discovering scientific data, it is
crucial that they be represented in a uniform way that can be queried
effectively. One step toward uniformly-represented metadata is to normalize the
multiple, distinct field names used in metadata (e.g., lat lon, lat and long)
to describe the same type of value. To that end, we present a new method based
on clustering and embeddings (i.e., vector representations of words) to align
metadata field names with ontology terms. We apply our method to biomedical
metadata by generating embeddings for terms in biomedical ontologies from the
BioPortal repository. We carried out a comparative study between our method and
the NCBO Annotator, which revealed that our method yields more and
substantially better alignments between metadata and ontology terms.
