AGROFAIR – Agro-Knowledge Integration: Developing a FAIR data science approach for adding value to the agricultural supply chain

Ficha técnica

Resumen

Farms are the engine to support rural employment making a considerable contribution to territorial development. Even though they have always been considered a cornerstone of agricultural activity in the European Union (EU) and in Latin America, this sector most often suffers from very low efficiency and effectiveness, sensitivity to weather, market disruptions and other external factors. Two different problems in knowledge sharing are present in this domain. First of all, the various interoperability regulations between the countries. Although some efforts are done to bypass this problem, like the EU-Mercosur signed in the summer of 2019, the different process semantics implemented in each region are a serious threat to the fulfilment of the process interoperability. Another problem is that in most of the cases, the knowledge transferred from generation to generation is paramount from a cultural point of view, but most of the time, it does not answer to the needs nor the requirements of the agri-food value chain. We aim at creating the core technology for a knowledge hub that integrates and aligns international regulations in agricultural activities, such as FAO's best practices, and possibly the last born EUMercosur regulations with the local restrictions, such as national policies, allowing the small farmers to access, in an easy way, a wider market through the certification of the practices and products. In order to develop this core technology, we propose to deploy various methodologies and tools working on the domains of knowledge formalisation, domain alignment and visualization. The domain formal representation allows for the semantic alignment of rules and restrictions from different institutional regulation bodies. Simultaneously, we will propose a model for incoherence detection letting us to highlight contradictory regulations. Those knowledge atoms and constructs will be represented through some visualization information interfaces according to the users’ needs.

Publicaciones

S. Liu, G. Zhao, H. Chen, A. Fernandez, D. Torres, L. Antonelli, H. Panetto and M. Lezoche, "Knowledge mobilisation crossing boundaries: a multi-perspective framework for agri-food value chains", Acta Horticulturae, pp. 185--200, 2020. 10.17660/actahortic.2021.1311.24 

Agro-Knowledge Integration: Developing a FAIR data science approach for adding value to the agricultural supply chain Diego Torres, Mario Lezoche, Cesar Collazos, Victor Codocedo, Regina Motz, Leandro Antonelli, Herve Panetto and Alejandro Fernandez. Books of Abstracts RUC-APS 2021: International Conference in Technology Innovation and Uncertainty Management in Agri-Food Systems, The University of Liverpool, 2021. 

Leutwyler Nicolas, Lezoche Mario, Panetto Hervé, Torres Diego. Extending RCA algorithm to consider ternary relations Pages 203-209. ICIST 2022 - 12th International Conference on Information Society and Technology 

Becas y pasantías

Tesis y trabajos finales de carrera

Juan Diego Bravo Guevara y Elmer Jose Muñoz Zuñiga (Tesina de grado). Patrones de Diseño de Interfaces Web Personalizables Para Mejorar la Solución de Tareas Complejas en un Sistema de Servicio al Cliente en Línea Basado en Chatbots. Universidad del Cauca Facultad de Ingeniería Electrónica y Telecomunicaciones. Dirigido por Cesar Collazos. Co-Dirigido por Diego Torres. Duración: 27/10/2022 - en curso

Mariano Ferreirone (Doctorado). Intelligent formal analysis of heterogeneous data for semantic web. Doctorado Université de Lorena. Dirigido por Diego Torres, Mario Lezcohe, Hervé Panetto. Duración: 28/2/2022 - en curso

Nicolás Leutwyler (Doctorado). Formal methods for knowledge extraction and reuse from heterogeneous sources: applications to the semantic interoperability of distributed architectures. Doctorado de la Universidad de Lorena y Doctorado de la UNQ. Dirigido por Diego Torres, Mario Lezoche, Hervé Panetto. Duración: 24/11/2021 - en curso