Semantic interoperability for data analysis in the food supply chain

Jack Verhoosel, Michael Van Bekkum, Tim Verwaart

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)

Abstract

Food supply chains consist of many links and operate on a global scale with many stakeholders involved from farm to fork. Each stakeholder maintains data about food products that they handle, but this data is not transparently available to all stakeholders in the chain and trust in data sharing is low. In addition, there are various other data sources that contain interesting data for stakeholders in the food chain, such as import/export transactions, production (forecast) data, parcel crop information, local weather predictions and social media streams. To improve their production, growers and traders are very interested in trends in the market and activities in supply and demand. To make all stakeholders in the food chain benefit from these data sources and to share data more transparently, the Dutch horticulture and food domain is developing the HortiCube platform via which various data sources are made accessible to application developers using a secure, linked data application interface. This paper describes the design and engineering of the semantic approach to enable interoperability between data sources. This includes (1) a high-level design of the HortiCube, (2) the metadata ontology used for describing the contents of the data sources in the HortiCube, (3) the common horticulture model used to achieve semantic alignment between data sources in the HortiCube, (4) a test application for a specific product case and (5) a discussion of our results and future work on this topic. The main contribution of our research is the generic solution and ontology design to the semantic challenges that arise when different data sources are combined to answer analysis questions for the user.

Original languageEnglish
Pages (from-to)101-111
Number of pages11
JournalInternational Journal on Food System Dynamics
Volume9
Issue number1
DOIs
Publication statusPublished - 2018

Keywords

  • Classifications mapping
  • Data analysis
  • Horticulture
  • Ontologies
  • Semantic alignment

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