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Mining the semantic Web for OWL axioms / Thu Huong Nguyen (2021)  

Public Titre : Mining the semantic Web for OWL axioms Titre original : Fouille du Web sémantique à la recherche d'axiomes OWL Type de document : Thèse/HDR Auteurs : Thu Huong Nguyen, Auteur ; Andrea Tettamanzi, Directeur de thèse Editeur : Nice : Université Côte d'Azur Année de publication : 2021 Importance : 175 p. Format : 21 x 30 cm Note générale : bibliographie Thèse de Doctorat présentée en vue de l’obtention du grade de docteur en Informatique de l’Université Côte d’Azur Langues : Français (fre) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] algorithme génétique [Termes IGN] données ouvertes [Termes IGN] exploration de données [Termes IGN] logique floue [Termes IGN] ontologie [Termes IGN] OWL [Termes IGN] RDF [Termes IGN] théorie des possibilités [Termes IGN] web des données [Termes IGN] web sémantique Index. décimale : THESE Thèses et HDR Résumé : (auteur) In the Semantic Web era, Linked Open Data (LOD) is its most successful implementation, which currently contains billions of RDF (Resource Data Framework) triples derived from multiple, distributed, heterogeneous sources. The role of a general semantic schema, represented as an ontology, is essential to ensure the correctness and consistency in LOD and make it possible to infer implicit knowledge by reasoning. The growth of LOD creates an opportunity for the discovery of ontological knowledge from its raw RDF data itself to enrich relevant knowledge bases. In this work, we aim at discovering schema-level knowledge in the form of axioms encoded in OWL (Ontology Web Language) from RDF data. The approaches to automated generation of the axioms from recorded RDF facts on the Web may be regarded as a case of inductive reasoning and ontology learning. The instances, represented by RDF triples, play the role of specific observations, from which axioms can be extracted by generalization. Based on the insight that discovering new knowledge is essentially an evolutionary, whereby hypotheses are generated by some heuristic mechanism and then tested against the available evidence, so that only the best hypotheses survive, we propose a model applying Grammatical Evolution, one type of evolutionary algorithm, to mine OWL axioms from an RDF data repository. In addition, we specialize the model for the specific problem of learning OWL class disjointness axioms, along with the experiments performed on DBpedia, one of the prominent examples of LOD. Furthermore, we use different axiom scoring functions based on possibility theory, which are well-suited to the open world assumption scenario of LOD, to evaluate the quality of discovered axioms. Specifically, we proposed a set of measures to build objective functions based on single-objective and multi-objective models, respectively. Finally, in order to validate it, the performance of our approach is evaluated against subjective and objective benchmarks, and is also compared to the main state-of-the-art systems. Note de contenu : 1- Introduction 2- Foundation 3- Literature review 4- Learning OWL axioms from RDF data 5- Axiom evaluation 6- Grammatical evolution models toward class disjointness axiom discovery 7- A multi-objective GE approach to class disjointness axioms discovery 8- Conclusions & perspectives Numéro de notice : 28614 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/SOCIETE NUMERIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : Côte d'Azur : 2021 Organisme de stage : I3S DOI : sans En ligne : https://hal.science/tel-03406784/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99492