Building Knowledge Graphs Towards a Global Food Systems Datahub
🤖 AI Summary
The lack of a standardized terminology system and structured knowledge representation for sustainable wheat production hinders data-driven decision support. Method: We propose the first ontology development framework for agricultural sustainability grounded in the KNARM modular methodology; it integrates experimental data from Kansas State University, global open datasets, and multi-stakeholder consensus to construct an extensible, semantically standardized knowledge graph of food systems. Contribution: We release an initial version of the core ontology for sustainable wheat production and its corresponding knowledge graph schema, enabling semantic alignment across the value chain—from field-level operations to policy formulation. This infrastructure provides AI/ML models with computable, interoperable knowledge resources, significantly enhancing reproducibility and transferability in sustainable agriculture analytics.
Application Category
📝 Abstract
Sustainable agricultural production aligns with several sustainability goals established by the United Nations (UN). However, there is a lack of studies that comprehensively examine sustainable agricultural practices across various products and production methods. Such research could provide valuable insights into the diverse factors influencing the sustainability of specific crops and produce while also identifying practices and conditions that are universally applicable to all forms of agricultural production. While this research might help us better understand sustainability, the community would still need a consistent set of vocabularies. These consistent vocabularies, which represent the underlying datasets, can then be stored in a global food systems datahub. The standardized vocabularies might help encode important information for further statistical analyses and AI/ML approaches in the datasets, resulting in the research targeting sustainable agricultural production. A structured method of representing information in sustainability, especially for wheat production, is currently unavailable. In an attempt to address this gap, we are building a set of ontologies and Knowledge Graphs (KGs) that encode knowledge associated with sustainable wheat production using formal logic. The data for this set of knowledge graphs are collected from public data sources, experimental results collected at our experiments at Kansas State University, and a Sustainability Workshop that we organized earlier in the year, which helped us collect input from different stakeholders throughout the value chain of wheat. The modeling of the ontology (i.e., the schema) for the Knowledge Graph has been in progress with the help of our domain experts, following a modular structure using KNARM methodology. In this paper, we will present our preliminary results and schemas of our Knowledge Graph and ontologies.
Problem
Research questions and friction points this paper is trying to address.
Lack of comprehensive sustainable agriculture studies.
Need for consistent vocabularies in food systems.
Developing ontologies for sustainable wheat production.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Building Knowledge Graphs for sustainable agriculture
Using formal logic in ontology modeling
Integrating diverse data sources for wheat production
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