Ontology-Based Knowledge Description Model for Climate Change

Surya, Deepak; Deepak, Gerard; Santhanavijayan, A.

doi:10.1007/978-3-030-71187-0_104

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…Pileggi developed a knowledge base of climate-change-related facts organized chronologically using their CCTL ontology [4]. Surya et al [5] incorporate sociogeological considerations into their climate change ontology model. Research in the semantic web community is more likely to develop ontology models for climate data based on specific use cases, which transforms the data schema into an ontology, potentially increasing data interoperability.…”

Section: Introductionmentioning

confidence: 99%

“…When applied to the NOAA original CSV dataset and enhanced with our climate KG atmospheric sectors, some common machine learning classification models offer many advantages over current weather prediction and classification tasks in terms of increased accuracy (up to 10% for rainfall detection), dependability, and reusability. A summary of the contributions of this article 5 to the climatic analysis field is listed as follows.…”

Section: Introductionmentioning

confidence: 99%

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Boosting Climate Analysis With Semantically Uplifted Knowledge Graphs

Orlandi

O’Sullivan

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%