Automatic hypothesis checking using escience research infrastructures, ontologies, and linked data: A case study in climate change research

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Abstract

Current research on physical or natural phenomena produces large datasets used by scientists to prove or disprove hypotheses. The increasing detail of our science triggers the trend of Big Data, and introduces new challenges on managing and processing that information. Allowing future reference to the data that generated previous research is a fundamental need and key feature of the scientific method, as experiments have to be reproduce efficiently by the research community. In this paper, we present a novel approach to manage such a large dataset in the climate research domain. With the help of ontologies and a Linked Data approach to describe the dataset, we allow computers to automatically prove hypothesis as new data of the same phenomena is integrated, realizing the vision of automated "executable research" that uses computational resources for continuously running exploratory hypotheses. © 2013 The Authors. Published by Elsevier B.V.

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Lappalainen, J., Sicilia, M. Á., & Hernández, B. (2013). Automatic hypothesis checking using escience research infrastructures, ontologies, and linked data: A case study in climate change research. In Procedia Computer Science (Vol. 18, pp. 1172–1178). Elsevier B.V. https://doi.org/10.1016/j.procs.2013.05.283

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