AILOSep 25, 2020

Towards a Modular Ontology for Space Weather Research

arXiv:2009.12285v21 citations
AI Analysis

This work addresses data integration problems for interdisciplinary researchers in space weather, but it is incremental as it builds on existing ontology methods.

The paper tackles the challenge of integrating heterogeneous data for space weather research by developing a modular ontology, demonstrating its adaptation to a specific use-case with existential rules and explicit typing.

The interactions between the Sun, interplanetary space, near Earth space environment, the Earth's surface, and the power grid are, perhaps unsurprisingly, very complicated. The study of such requires the collaboration between many different organizations spanning the public and private sectors. Thus, an important component of studying space weather is the integration and analysis of heterogeneous information. As such, we have developed a modular ontology to drive the core of the data integration and serve the needs of a highly interdisciplinary community. This paper presents our preliminary modular ontology, for space weather research, as well as demonstrate a method for adaptation to a particular use-case, through the use of existential rules and explicit typing.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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