Boosting Climate Analysis With Semantically Uplifted Knowledge Graphs

Nowadays, the fast expansion of heterogeneous climate data resources accessible on the Internet has led to substantial data fragmentation on the web. For example, station-based sensor data from different sources are likely to be interrelated but may be stored in disparate formats, such as CSV , JSON...

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Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing 2022, Vol.15, p.4708-4718
Main Authors: Wu, Jiantao, Orlandi, Fabrizio, O'Sullivan, Declan, Pisoni, Enrico, Dev, Soumyabrata
Format: Article
Language:English
Subjects:
Analysis
Climate change
Climate data
Climate models
Climatic data
Data analysis
Data exchange
Graphs
Interoperability
knowledge graphs (KGs)
Knowledge representation
Learning algorithms
Linked data
Machine learning
Meteorology
Ontologies
Rain
Rainfall
Resource description framework
semantic webs
Semantics
Streamlines
Tables (data)
Task analysis
Workflow
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Summary:Nowadays, the fast expansion of heterogeneous climate data resources accessible on the Internet has led to substantial data fragmentation on the web. For example, station-based sensor data from different sources are likely to be interrelated but may be stored in disparate formats, such as CSV , JSON , and XML . To address the data isolation problem, several semantically uplifted knowledge graphs are proposed for climate data exchange. While these knowledge graphs improve data interoperability, the advancement in multisource data interchange is limited to data included inside knowledge graphs. As a result, the exclusive interoperability of current climatic knowledge graphs hampers the flow of data into typical climate analysis workflows in contexts, where analytical models often need data in nonknowledge graph formats. This article addresses this issue by focusing on enhancing climate analysis workflows within the context of the Python machine learning environment, with a preference for tabular data. We propose an analysis workflow able to automatically integrate remote climate knowledge graph data with local tabular data so as to enhance the data usability with respect to certain climate analysis tasks. To underscore the importance of our study, we illustrate how the workflow streamlines the access to multisource climatic variables in the Python environment from a semantic perspective. The additional knowledge graph data have the potential to augment local datasets in the climate domain, as evidenced by an improvement in accuracy of up to 10% for machine learning geared on rainfall detection.
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2022.3177463