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 <monosp...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9780580/ |
| _version_ | 1811241920837451776 |
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| author | Jiantao Wu Fabrizio Orlandi Declan O'Sullivan Enrico Pisoni Soumyabrata Dev |
| author_facet | Jiantao Wu Fabrizio Orlandi Declan O'Sullivan Enrico Pisoni Soumyabrata Dev |
| author_sort | Jiantao Wu |
| collection | DOAJ |
| description | 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 <monospace>CSV</monospace>, <monospace>JSON</monospace>, and <monospace>XML</monospace>. 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. |
| first_indexed | 2024-04-12T13:44:12Z |
| format | Article |
| id | doaj.art-cec315d8fbcc4513bc44fc8f2c4c696c |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-04-12T13:44:12Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-cec315d8fbcc4513bc44fc8f2c4c696c2022-12-22T03:30:46ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352022-01-01154708471810.1109/JSTARS.2022.31774639780580Boosting Climate Analysis With Semantically Uplifted Knowledge GraphsJiantao Wu0https://orcid.org/0000-0002-3659-6630Fabrizio Orlandi1Declan O'Sullivan2https://orcid.org/0000-0003-1090-3548Enrico Pisoni3https://orcid.org/0000-0001-5484-5744Soumyabrata Dev4https://orcid.org/0000-0002-0153-1095ADAPT SFI Research Centre, School of Computer Science, University College Dublin, Dublin, IrelandADAPT SFI Research Centre, School of Computer Science and Statistics, Trinity College Dublin, Dublin, IrelandADAPT SFI Research Centre, School of Computer Science and Statistics, Trinity College Dublin, Dublin, IrelandEuropean Commission Joint Research Centre, Ispra, ItalyADAPT SFI Research Centre, School of Computer Science, University College Dublin, Dublin, IrelandNowadays, 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 <monospace>CSV</monospace>, <monospace>JSON</monospace>, and <monospace>XML</monospace>. 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.https://ieeexplore.ieee.org/document/9780580/Climate dataknowledge graphs (KGs)linked datamachine learningsemantic webs |
| spellingShingle | Jiantao Wu Fabrizio Orlandi Declan O'Sullivan Enrico Pisoni Soumyabrata Dev Boosting Climate Analysis With Semantically Uplifted Knowledge Graphs IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Climate data knowledge graphs (KGs) linked data machine learning semantic webs |
| title | Boosting Climate Analysis With Semantically Uplifted Knowledge Graphs |
| title_full | Boosting Climate Analysis With Semantically Uplifted Knowledge Graphs |
| title_fullStr | Boosting Climate Analysis With Semantically Uplifted Knowledge Graphs |
| title_full_unstemmed | Boosting Climate Analysis With Semantically Uplifted Knowledge Graphs |
| title_short | Boosting Climate Analysis With Semantically Uplifted Knowledge Graphs |
| title_sort | boosting climate analysis with semantically uplifted knowledge graphs |
| topic | Climate data knowledge graphs (KGs) linked data machine learning semantic webs |
| url | https://ieeexplore.ieee.org/document/9780580/ |
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