A Knowledge Graph Informing Soil Carbon Modeling

2025-08-14

AI Summary

Soil organic carbon (SOC) dynamics research is hindered by challenges in integrating heterogeneous, multi-source data and achieving semantic interoperability. This paper introduces SOCKG—the first semantic knowledge graph framework tailored for SOC modeling. It employs an OWL ontology to semantically align experimental datasets with the U.S. National Agricultural Library’s controlled vocabulary, markedly enhancing data reusability and interoperability. A dual-engine knowledge graph—built on GraphDB and Neo4j—supports SPARQL querying, RDF access, and interactive visual analytics. Crucially, SOCKG enables, for the first time, semantic comparative analysis of SOC changes across field sites and experimental treatments. This work establishes a scalable knowledge infrastructure for climate change mitigation and sustainable agriculture research, significantly improving both the efficiency of SOC data integration and the depth of analytical insights.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Ontologies
• Machine Learning: Graph-based Machine Learning

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT

Abstract

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.

Problem

• Integrating complex soil carbon data from multiple sources
• Creating a queryable knowledge graph for agricultural research
• Improving interoperability and analysis of soil organic carbon dynamics

Innovation

• Semantic infrastructure for soil carbon data integration
• Ontological model mapping agricultural experimental datasets
• GraphDB and Neo4j enabling advanced querying capabilities