An Ontology-based Visual Analytics for Apple Variety Testing

vPICO presentation on EGU 2021

Abstract

This study extends the visual analytics approach with a structural way of data organization (ontologies), data mining, and visualization techniques to retrieve knowledge from an extensive collection of apple variety testing program and environmental data. The prototype stands on three main components, namely ontology, data analysis, and data visualization. Ontologies provide a representation of expert knowledge and create standard concepts for data integration, opening the possibility to share the knowledge using a unified terminology and allowing for inference. Building upon relevant semantic models (e.g., agri-food experiment ontology, plant trait ontology, GeoSPARQL), we propose to extend them based on the apple variety testing and climate data. Data integration and harmonization through developing an ontology-based model provides a framework for integrating relevant concepts and relationships between them, data sources from different repositories, and defining a precise specification for the knowledge retrieval. Besides, as the variety testing is performed on different locations, the geospatial component can enrich the analysis with spatial properties. Furthermore, the visual narratives designed within this study will give a better-integrated view of data entities' relations and the meaningful patterns and clustering based on semantic concepts.

Publication
In EGU General Assembly 2021
Ekaterina Chuprikova
Ekaterina Chuprikova
Marie Sklodowska-Curie Fellow

My research interests include distributed robotics, mobile computing and programmable matter.

Abraham Mejia Aguilar
Abraham Mejia Aguilar
Senior researcher
Roberto Monsorno
Roberto Monsorno
Head of the Center for Sensing Solutions