METHOD OF AUTOMATED DEVELOPMENT AND EVALUATION OF ONTOLOGIES’ QUALITIES OF KNOWLEDGE BASES

The process of automated development of base ontology is considered. It has been offered to consider the concepts and elements of ontologies for increasing the effectiveness of knowledge bases, the core of which is the ontology. Methods of specifying the weights of the relevant elements and optimization the structure of knowledge base of ontologies has been elaborated. It has been offered to evaluate the quality of the ontologies based on ISO 9126

Knowledge discovery through ontology matching: An approach based on an Artificial Neural Network model

The fundamental principle of the Semantic Web is the creation and use of semantic annotations connected to formal descriptions, such as domain ontologies. The lack of an integrated view of all web nodes and the existence of heterogeneous domain ontologies drive new challenges in the discovery of knowledge resources, which are relevant to a user´s request. New eficient approaches for developing web intelligence and helping users to avoid irrelevant search results on the web have recently appeared. Artificial Neural Networks (ANN) being one of the most recent ones. However,there still remains a lot of work to be done in this area. This work makes a contribution to the field of knowledge-resource discovery and ontology matching techniques for the Semantic Web by presenting an approach which is based on an ANN classifier. Experimental results show that the ANN-based ontology matching model has provided satisfactory responses to the test cases.Fil: Rubiolo, Mariano. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Centro de Investigación y Desarrollo de Ingeniería en Sistemas de Información; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Caliusco, Maria Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Stegmayer, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Coronel, M.. Universidad Tecnológica Nacional; ArgentinaFil: Gareli Fabrizi, M.. Universidad Tecnológica Nacional; Argentin

Responsive Production in Manufacturing: A Modular Architecture

[EN] This paper proposes an architecture aiming at promoting the convergence of the physical and digital worlds, through CPS and IoT technologies, to accommodate more customized and higher quality products following Industry 4.0 concepts. The architecture combines concepts such as cyber-physical systems, decentralization, modularity and scalability aiming at responsive production. Combining these aspects with virtualization, contextualization, modeling and simulation capabilities it will enable self-adaptation, situational awareness and decentralized decision-making to answer dynamic market demands and support the design and reconfiguration of the manufacturing enterprise.The research leading to these results has received funding from the European Union H2020 project C2 NET (FoF-01-2014) nr 636909.Marques, M.; Agostinho, C.; Zacharewicz, G.; Poler, R.; Jardim-Goncalves, R. (2018). Responsive Production in Manufacturing: A Modular Architecture. 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