2 research outputs found
Ontology-Grounded Topic Modeling for Climate Science Research
In scientific disciplines where research findings have a strong impact on
society, reducing the amount of time it takes to understand, synthesize and
exploit the research is invaluable. Topic modeling is an effective technique
for summarizing a collection of documents to find the main themes among them
and to classify other documents that have a similar mixture of co-occurring
words. We show how grounding a topic model with an ontology, extracted from a
glossary of important domain phrases, improves the topics generated and makes
them easier to understand. We apply and evaluate this method to the climate
science domain. The result improves the topics generated and supports faster
research understanding, discovery of social networks among researchers, and
automatic ontology generation.Comment: To appear in Proc. of Semantic Web for Social Good Workshop of the
Int. Semantic Web Conf., Oct 2018 and published as part of the book "Emerging
Topics in Semantic Technologies. ISWC 2018 Satellite Events", E. Demidova,
A.J. Zaveri, E. Simperl (Eds.), ISBN: 978-3-89838-736-1, 2018, AKA Verlag
Berlin, (edited authors
Machine Learning for the Geosciences: Challenges and Opportunities
Geosciences is a field of great societal relevance that requires solutions to
several urgent problems facing our humanity and the planet. As geosciences
enters the era of big data, machine learning (ML) -- that has been widely
successful in commercial domains -- offers immense potential to contribute to
problems in geosciences. However, problems in geosciences have several unique
challenges that are seldom found in traditional applications, requiring novel
problem formulations and methodologies in machine learning. This article
introduces researchers in the machine learning (ML) community to these
challenges offered by geoscience problems and the opportunities that exist for
advancing both machine learning and geosciences. We first highlight typical
sources of geoscience data and describe their properties that make it
challenging to use traditional machine learning techniques. We then describe
some of the common categories of geoscience problems where machine learning can
play a role, and discuss some of the existing efforts and promising directions
for methodological development in machine learning. We conclude by discussing
some of the emerging research themes in machine learning that are applicable
across all problems in the geosciences, and the importance of a deep
collaboration between machine learning and geosciences for synergistic
advancements in both disciplines.Comment: Under review at IEEE Transactions on Knowledge and Data Engineerin