- author = "Thu Huong Nguyen",
- title = "Mining the semantic Web for {OWL} axioms",
- titletranslation = "Fouille du Web semantique a la recherche d'axiomes OWL",
- school = "Universite Cote d'Azur",
- year = "2021",
- month = jul # "~02",
- address = "France",
- keywords = "genetic algorithms, genetic programming, grammatical evolution, semantic web, OWL, RDF, description logics, ontology, ontology learning, ontology enrichment, axioms, class disjointness axioms, data mining, evolutionary algorithms, fuzzy sets, possibility theory, web semantique, logiques de description, ontologies, apprentissage d'ontologies, enrichissement d'ontologie, axiomes, axiomes de disjonction de classe, exploration des donnees, algorithmes evolutionnaires, evolution grammaticale, programmation genetique, ensembles flous, theorie des possibilites, [INFO, info-wb]computer science [cs]/web, info-ds]computer science [cs]/data structures and algorithms [cs, DS], info-lg]computer science [cs]/machine learning [cs, LG]",
- ISSN = "03406784",
- bibsource = "OAI-PMH server at api.archives-ouvertes.fr",
- contributor = "Communities and Semantics Web-Instrumented Man-Machine Interactions and Andrea Tettamanzi",
- identifier = "NNT: 2021COAZ4052; tel-03406784",
- language = "en",
- oai = "oai:HAL:tel-03406784v1",
- rights = "info:eu-repo/semantics/OpenAccess",
-
URL = "
https://tel.archives-ouvertes.fr/tel-03406784",
-
URL = "https://tel.archives-ouvertes.fr/tel-03406784/document",
-
URL = "
https://tel.archives-ouvertes.fr/tel-03406784/file/2021COAZ4052.pdf",
- size = "175 pages",
-
abstract = "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
generalisation.
Based on the insight that discovering new knowledge is essentially an evolutionary process, 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.",
-
resume = "A l'ere du Web Semantique, les donnees liees ouvertes
(LOD) en sort l'implementation la plus reussie qui
contient actuellement des milliards de triplets RDF
(Resource Data Framework) derives de sources multiples,
distribuees et heterogenes. Le role d'un schema
semantique general, represente comme une ontologie, est
essentiel pour assurer l'exactitude et la coherence du
LOD et permettre d'inferer des connaissances implicites
par le raisonnement. La croissance du LOD a cree une
opportunite pour la decouverte de connaissances
ontologiques a partir de ses donnees RDF brutes
elles-memes pour enrichir les bases de connaissances
pertinentes. Dans ce travail, nous visons a decouvrir
des connaissances au niveau des schemas sous forme
d'axiomes encodes en OWL (Ontology Web Language) a
partir de donnees RDF. Les approches de generation
automatisee d'axiomes a partir de faits RDF enregistres
sur le Web peuvent etre considerees comme un cas de
raisonnement inductif et d'apprentissage d'ontologie.
Les instances, representees par des triplets RDF,
jouent le role d'observations specifiques, dont les
axiomes peuvent etre extraits par
generalisation.
Partant du principe que la decouverte de nouvelles connaissances est essentiellement un processus evolutif, dans lequel les hypotheses sont generees par un mecanisme heuristique puis testees par rapport a l'evidence disponible, de sorte que seules les meilleures hypotheses survivent, nous proposons un modele appliquant l'evolution grammaticale, un type d'algorithme evolutionnaire, pour extraire les axiomes OWL d'un referentiel de donnees RDF. En outre, nous specialisons le modele pour le probleme specifique d'apprentissage d'axiomes de disjonction de classes OWL, ainsi que pour les experiences effectuees sur DBpedia, l'un des exemples les plus proeminents du LOD.
De plus, nous utilisons differentes fonctions de notation des axiomes basees sur la theorie des possibilites, qui sont bien adaptees au scenario d'hypothese du monde ouvert du LOD, pour evaluer la qualite des axiomes decouverts. Plus precisement, nous avons propose un ensemble de mesures pour construire des fonctions objectif basees respectivement sur des modeles a objectif unique et multi-objectifs.
Enfin, afin de la valider, la performance de notre approche est evaluee par rapport a des benchmarks, subjectif et objectif, et est egalement comparee aux principaux systemes de l'etat de l'art.",
- notes = "Supervisor: Andrea G.B. Tettamanzi",
