Tesi di Dottorato

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Policy tesi di dottorato

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Author: search.filters.author.Greco, GianluigiStart date: 2022

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    Arsenic Ore Mixture Froth Image Generation with Neural Networks and a Language for Declarative Data Validation
    (Università della Calabria, 2022-04-14) Zamayla, Arnel; Greco, Gianluigi; Alviano, Mario; Dodaro, Carmine
    Computer vision systems that measure froth flow velocities and stability designed for flotation froth image analysis are well established in industry, as they are used to control material recovery. However flotation systems that has limited data has not been explored in the same fashion bearing the fact that big data tools like deep convolutional neural networks require huge amounts of data. This lead to the motivation of the research reported in the first part of this thesis, which is to generate synthetic images from limited data in order to create a froth image dataset. The image synthesis is possible through the use of generative adversarial network. The performance of human experts in this domain in identifying the original and synthesized froth images were then compared with the performance of the models. The models exhibited better accuracy levels by average on the tests that were performed. The trained classifier was also compared with some of the established neural network models in the literature like the AlexNet, VGG16 ang ResNet34. Transfer learning was used as a method for this purpose. It also showed that these pretrained networks that are readily available have better accuracy by average comapared to trained experts. The second part of this thesis reports on a language designed for data validation in the context of knowledge representation and reasoning. Specifically, the target language is Answer Set Programming (ASP), a logic-based programming language widely adopted for combinatorial search and optimization, which however lacks constructs for data validation. The language presented in this thesis fulfills this gap by introducing specific constructs for common validation criteria, and also supports the integration of consolidated validation libraries written in Python. Moreover, the language is designed so to inject data validation in ordinary ASP programs, so to promote fail-fast techniques at coding time without imposing any lag on the deployed system if data are pretended to be valid.
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    Large-scale ontology-mediated query answering over OWL 2 RL ontologies
    (Università della Calabria, 2022-03-11) Fiorentino, Alessio; Greco, Gianluigi; Manna, Marco
    Ontology-mediated query answering (OMQA) is an emerging paradigm at the basis of many semantic-centric applications. In this setting, a conjunctive query has to be evaluated against a logical theory (knowledge base) consisting of an extensional database paired with an ontology, which provides a semantic conceptual view of the data. Among the formalisms that are capable to express such a conceptual layer, the Web Ontology Language OWL is certainly the most popular one. Reasoning over OWL is a very expensive task, in general. For that reason, expressive yet decidable fragments of OWL have been identi ed. Among them, we focus on OWL 2 RL, which o ers a rich variety of semantic constructors, apart from supporting all RDFS datatypes. Although popular Web resources|such as DBpedia|fall in OWL 2 RL, only a few systems have been designed and implemented for this fragment. None of them, however, fully satisfy all the following desiderata: (i) being freely available and regularly maintained; (ii) supporting SPARQL queries; (iii) properly applying the sameAs property without adopting the unique name assumption; (iv) dealing with concrete datatypes. This thesis aims to provide a contribution in this setting. Primarily, we present DaRLing: an open-source Datalog rewriter for OWL 2 RL ontological reasoning under SPARQL queries. We describe its architecture, the rewriting strategies it implements, and the result of an experimental evaluation that demonstrates its practical applicability. Then, to reduce memory consumption and possibly optimize execution times of Datalog queries over large databases, we introduce novel techniques to determine an optimal indexing schema together with suitable body-orderings for Datalog rules, based on the concept of optimal evaluation plan. The ASP encoding of a planner for the computation of such plans is provided and explained in detail. The new approach is then compared with the standard execution plans implemented in stat-of-the-art Datalog systems over widely used ontological benchmarks.
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    A logic-based decision support system for the diagnosis of headache disorders according to the ichd - 3 international classification
    (Università della Calabria, 2022-04-21) Costabile, Roberta; Manna, Marco; Greco, Gianluigi
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    Dyadic TGDs - A new paradigm for ontological query answering
    (Università della Calabria, 2022-03-11) Marte, Cinzia; Greco, Gianluigi; Manna, Marco; Guerriero, Francesca; Leone, Nicola
    Ontology-BasedQueryAnswering(OBQA)consistsinqueryingdata– bases bytakingontologicalknowledgeintoaccount.Wefocusona logical frameworkbasedonexistentialrulesor tuple generatingdepen- dencies (TGDs), alsoknownasDatalog±, whichcollectsthebasicde- cidable classesofTGDs,andgeneralizesseveralontologyspecification languages. While thereexistlotsofdifferentclassesintheliterature,inmost cases eachofthemrequiresthedevelopmentofaspecificsolverand, only rarely,thedefinitionofanewclassallowstheuseofexisting systems. Thisgapbetweenthenumberofexistentparadigmsandthe numberofdevelopedtools,promptedustodefineacombinationof Shy and Ward (twowell-knownclassesthatenjoygoodcomputational properties)withtheaimofexploitingthetooldevelopedfor Shy. Nevertheless,studyinghowtomergethesetwoclasses,wehavereal- ized thatitwouldbepossibletodefine,inamoregeneralway,the combinationofexistingclasses,inordertomakethemostofexisting systems. Hence, inthiswork,startingfromtheanalysisofthetwoaforemen- tioned existingclasses,wedefineamoregeneralclass,named Dyadic TGDs, thatallowstoextendinauniformandelegantwayallthede- cidable classes,whileusingtheexistentrelatedsystems.Atthesame time, wedefinealsoacombinationof Shy and Ward, named Ward+, and weshowthatitcanbeseenasaDyadicsetofTGDs. Finally,tosupportthetheoreticalpartofthethesis,weimplementa BCQ evaluationalgorithmfortheclass Ward+, thattakesadvantage of anexistingsolverdevelopedfor Shy.