Enhancing NASA Earth Science Data Discovery from Scientific Publications

Earth observations from space borne instruments have evolved
explosively in the past decades. Following closely are reanalysis
systems assimilating model and observational data, yielding
even longer records and larger number of variables. Thanks to
advances in internet technology, it is now easier than ever to
visualize and analyze these data using web interfaces. On the
other hand, it also becomes an increasingly daunting task to
build upon the existing knowledge published in various peer reviewed
sources, and navigate toward the most relevant data,
analysis, and visualization.
We present an analysis of a subset of publications that utilized a
popular visualization web interface at the NASA Goddard Earth
Science Data and Information Services Center. Known as
"Giovanni", it allows researchers from wide backgrounds to work
with hundreds of variables from space observations and
assimilation systems.
Since coming online more than a decade ago, Giovanni has been
credited in more than 100 papers per year, and the total count
now is estimated to be nearly 1,500. Many of these papers
contain valuable information about when, where and how
Giovanni has been used, and hence forge an opportunity to learn
and share the knowledge of which variables were used for what
research projects.
The purpose of our work is to retrieve the information from the
papers and organize it as a knowledge repository which links
together datasets, variables, places, dates and phenomena all of
which reflect the essence of the published research. Since the
publications are unstructured texts, we use natural language
processing along with machine learning methods in the retrieval
process. One of the challenges is deciphering the dataset
names, because in many cases researchers refer to variables,
rather than the datasets containing them. To constrain the
number of terms, we deploy Earth Science ontologies as
dictionaries for the term extraction. We demonstrate that storing
these terms and underlying ontologies, along with datasets,
variables and papers in the knowledge graph database, enables
various linkages between all these entities facilitating the data
discovery.
Thus, we are setting a qualitatively new stage in improvements
of web data interfaces, where machine learning techniques are
used to establish and optimize usage-based discovery of data.