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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.10965 |
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| _version_ | 1866908490180591616 |
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| author | Shirvani-Mahdavi, Nasim Wingfield, Devin Gutierrez, Juan Guajardo Tran, Mai Zhu, Zhengyuan Zhang, Zeyu Zhang, Haiqi Goudar, Abhishek Divakar Li, Chengkai Jin, Virginia Propst, Timothy Roberts, Dan Stewart, Catherine Su, Jianzhong Woodward-Greene, Jennifer |
| author_facet | Shirvani-Mahdavi, Nasim Wingfield, Devin Gutierrez, Juan Guajardo Tran, Mai Zhu, Zhengyuan Zhang, Zeyu Zhang, Haiqi Goudar, Abhishek Divakar Li, Chengkai Jin, Virginia Propst, Timothy Roberts, Dan Stewart, Catherine Su, Jianzhong Woodward-Greene, Jennifer |
| contents | Soil organic carbon is crucial for climate change mitigation and agricultural sustainability. However, understanding its dynamics requires integrating complex, heterogeneous data from multiple sources. This paper introduces the Soil Organic Carbon Knowledge Graph (SOCKG), a semantic infrastructure designed to transform agricultural research data into a queryable knowledge representation. SOCKG features a robust ontological model of agricultural experimental data, enabling precise mapping of datasets from the Agricultural Collaborative Research Outcomes System. It is semantically aligned with the National Agricultural Library Thesaurus for consistent terminology and improved interoperability. The knowledge graph, constructed in GraphDB and Neo4j, provides advanced querying capabilities and RDF access. A user-friendly dashboard allows easy exploration of the knowledge graph and ontology. SOCKG supports advanced analyses, such as comparing soil organic carbon changes across fields and treatments, advancing soil carbon research, and enabling more effective agricultural strategies to mitigate climate change. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_10965 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Knowledge Graph Informing Soil Carbon Modeling Shirvani-Mahdavi, Nasim Wingfield, Devin Gutierrez, Juan Guajardo Tran, Mai Zhu, Zhengyuan Zhang, Zeyu Zhang, Haiqi Goudar, Abhishek Divakar Li, Chengkai Jin, Virginia Propst, Timothy Roberts, Dan Stewart, Catherine Su, Jianzhong Woodward-Greene, Jennifer Computers and Society Symbolic Computation Soil organic carbon is crucial for climate change mitigation and agricultural sustainability. However, understanding its dynamics requires integrating complex, heterogeneous data from multiple sources. This paper introduces the Soil Organic Carbon Knowledge Graph (SOCKG), a semantic infrastructure designed to transform agricultural research data into a queryable knowledge representation. SOCKG features a robust ontological model of agricultural experimental data, enabling precise mapping of datasets from the Agricultural Collaborative Research Outcomes System. It is semantically aligned with the National Agricultural Library Thesaurus for consistent terminology and improved interoperability. The knowledge graph, constructed in GraphDB and Neo4j, provides advanced querying capabilities and RDF access. A user-friendly dashboard allows easy exploration of the knowledge graph and ontology. SOCKG supports advanced analyses, such as comparing soil organic carbon changes across fields and treatments, advancing soil carbon research, and enabling more effective agricultural strategies to mitigate climate change. |
| title | A Knowledge Graph Informing Soil Carbon Modeling |
| topic | Computers and Society Symbolic Computation |
| url | https://arxiv.org/abs/2508.10965 |