Examinando por Autor "Panetto, Herve"
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Acceso Abierto Agro-Knowledge Integration: Developing a FAIR data science approach for adding value to the agricultural supply chain(2021) Torres, Diego; Lezoche, Mario; Collazos, César; Codocedo, Víctor; Motz, Regina; Antonelli, Leandro; Panetto, Herve; Fernández, AlejandroFarms 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, 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 fulfillment 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 EU-Mercosur 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 formalization, domain alignment and visualization. The domain of 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. The methods and tools that will be employed are at the same time the pillars from the multi relational data mining (MRDM), the artificial intelligence (AI), the knowledge formalization (KF) domains, but will extend the interoperability properties of those domains to become a new interesting and valuable tool for the presented problem. This abstract is issued from an accepted Stic-AmSud project that wad elaborated during the secondments of the RUC-APS project. - Resumen
Acceso Abierto Knowledge discovering from multiple sources in agriculture value-chain(2021) Torres, Diego; Lezoche, Mario; Fernández, Alejandro; Antonelli, Leandro; Panetto, HerveThe agri-food value-chain results from the interaction of multiple stakeholders. Each stakeholder contributes with a distinct perspective and interest. The diversity in activities and work forms in the value-chain results in a wide variety of data sources, and data management practices. It is common to find information managed in databases, document repositories, or even social media. Document formats also vary (e.g., CSV, PDF, XML, etc.), and so do content types (e.g., graphics, tables, lists, images, etc.). In this context, effective decision-making relies heavily on the availability of interoperable, comprehensive, accurate, and timely information. Knowledge graphs (KG) are graphbased data models for knowledge extraction from multiple structured and unstructured sources that support multilingual integration. KG are frequently combined with knowledge discovering approaches like embedding and multi-relational data mining methods like the Formal Concept Analysis (FCA) and its extension the Relational Concept Analysis (RCA). This work proposes an automatic pipeline process to combine and align different agri-food information sources to discover new pieces of knowledge based on KG and RCA. The approach combines several research lines: (1) entities and relations detection in different sources; (2) alignment with a shared ontology description, based on GACS and AGROVOC, and (3) discovering new knowledge with Relational Concept Analysis in the shape of association rules formalized following the description logic. - Documento de conferencia
Acceso Abierto Scenarios, shared understanding, and group decision support to foster innovation networks(2023) Agredo-Delgado, Vanessa; Antonelli, Leandro; Collazos, César A.; Fernández, Alejandro; Zaraté, Pascale; Camiller, Guy; Hurtado, Julio; Lezoche, Mario; Motz, Regina; Panetto, Herve; Torres, DiegoCollaborative innovation involves diverse individuals and organizations working together to develop new ideas, products, or services. Successful collaboration in networked innovation projects is challenging due to the need to cross the knowledge boundaries that exist between organizations, disciplines, and cognitive frames. We propose an approach to support knowledge mobilization and learning in networked innovation projects. Scenarios, stored in a shared repository, are used to capture and share information about application and solution domains. A collaborative process guides participants to reach a shared understanding and construct shared meaning. Stakeholders engage in a collaborative decision-making process of scenario ranking that includes identifying and negotiating comparison criteria. Although the approach is presented with examples in the domain of agriculture, where validation of the constituent elements took place, it is domain independent.