Global agricultural concept space: lightweight semantics for pragmatic interoperability

Abstract

Progress on research and innovation in food technology depends increasingly on the use of structured vocabularies—concept schemes, thesauri, and ontologies—for discovering and re-using a diversity of data sources. Here, we report on GACS Core, a concept scheme in the larger Global Agricultural Concept Space (GACS), which was formed by mapping between the most frequently used concepts of AGROVOC, CAB Thesaurus, and NAL Thesaurus and serves as a target for mapping near-equivalent concepts from other vocabularies. It provides globally unique identifiers, which can be used as keywords in bibliographic databases, tags for web content, for building lightweight facet schemes, and for annotating spreadsheets, databases, and image metadata using synonyms and variant labels in 25 languages. The minimal semantics of GACS allows terms defined with more precision in ontologies, or less precision in controlled vocabularies, to be linked together making it easier to discover and integrate semantically diverse data sources.

Data sharing in agricultural supply chains: Using semantics to enable sustainable food systems

“…It is currently the most comprehensive multilingual thesaurus and vocabulary for agriculture. AGROVOC has now been partially mapped onto the US National Agricultural Library of the USDA and the CABI thesaurus in the form of the GACS ontology which has mapped and integrated the top 15,000 concepts [4]. Other recent work in this area has also focused on developing ontologies for sharing of research data including the Crop ontology initiative, the Agronomy Ontology (AgrO), and the Plant Trait Ontology (TO) supported by CGIAR [2].…”

Setting data free: The politics of open data for food and agriculture

“…As the chief technical officer of an ag-tech startup explained to us, in the United Kingdom, a cow shed “literally that is a shed,” whereas in New Zealand it refers to a “milking parlor,” a difference in meaning great enough to produce data incompatibilities even between these two English-speaking countries with a shared colonial history. Agricultural ontologies seek to solve such incompatibilities by establishing a shared vocabulary across different datasets, permitting easier aggregation, comparison, and analysis while ensuring that data are machine readable and interoperable across platforms (Baker et al, 2019). There are a few widely accepted agri-food ontologies already in existence, and many more under development (Arnaud et al, 2020; Jebaraj and Sathiaseelan, 2017).…”

A Data Ecosystem for Orchard Research and Early Fruit Traceability

“…The technology platforms used to support the implementation of the information model were the ESRI ArcGIS Enterprise suite version 10.9.1 and Microsoft [19] SQL 12 as the foundation database platform. It was estimated that the amount of measurement data within a research season would be in the order of 1 million records and that the SQL server would easily accommodate this volume and support the relationships required for data integration.…”

“…Furthermore, this component of the schema was informed by following the initiatives in record-keeping requirements containing key data elements (KDEs) associated with specific critical tracking events (CTEs) defined by the US FDA's New Era of Smarter Food Safety Blueprint and Section 204 (d) of the FDA Food Safety Modernization Act (FSMA). However, the approach to schema design and associated semantics development was in the form of "lightweight semantics" to allow progress toward a form of pragmatic interoperability [19]. In The ISO 19156:2011 Observation and Measurements (O&M) standard [3] was used as a foundation to develop the information model and schema to support measurement data.…”