Mariano Rubiolo

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With the emergence of the Semantic Web several domain ontologies were developed, which varied not only in their structure but also in the natural language used to define them. 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(More)
— Model compression is a required task when slow and large models are used, for example, for classification, but there are transmissions, space, time or computing capabilities constraints that have to be fulfilled. Multilayer Perceptron (MLP) models have been traditionally used as classifiers. Depending on the problem, they may need a large number of(More)
Model compression is required when large models are used, for example, for a classification task, but there are transmission, space, time, or computing constraints that have to be fulfilled. Multilayer perceptron (MLP) models have been traditionally used as classifiers. Depending on the problem, they may need a large number of parameters (neuron functions,(More)
Discovering gene regulatory networks from data is one of the most studied topics in recent years. Neural networks can be successfully used to infer an underlying gene network by modeling expression profiles as times series. This work proposes a novel method based on a pool of neural networks for obtaining a gene regulatory network from a gene expression(More)
This paper describes our continuing research on ontology-based knowledge source discovery on the Semantic Web. The research documented here is focused on discovering distributed knowledge sources from a user query using an Articial Neural Network model. An experience using the Wordnet multilingual database for the translation of the terms extracted from the(More)
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