Mining Significant Maximum Cardinalities in Knowledge Bases - Université de Tours Accéder directement au contenu
Communication Dans Un Congrès Année : 2019

Mining Significant Maximum Cardinalities in Knowledge Bases

Résumé

Semantic Web connects huge knowledge bases whose content has been generated from collaborative platforms and by integration of heterogeneous databases. Naturally, these knowledge bases are incomplete and contain erroneous data. Knowing their data quality is an essential long-term goal to guarantee that querying them returns reliable results. Having cardinality constraints for roles would be a significant advance to distinguish correctly and completely described individuals from those having data either incorrect or insufficiently informed. In this paper, we propose a method for automatically discovering from the knowledge base’s content the maximum cardinality of roles for each concept, when it exists. This method is robust thanks to the use of Hoeffding’s inequality. We also design an algorithm, named C3M, for an exhaustive search of such constraints in a knowledge base benefiting from pruning properties that drastically reduce the search space. Experiments conducted on DBpedia demonstrate the scaling up of C3M, and also highlight the robustness of our method, with a precision larger than 95%.

Domaines

Informatique
Fichier principal
Vignette du fichier
iswc19.pdf (383.85 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-02269830 , version 1 (18-02-2022)

Identifiants

Citer

Arnaud Giacometti, Béatrice Markhoff, Arnaud Soulet. Mining Significant Maximum Cardinalities in Knowledge Bases. International Semantic Web Conference (ISWC), Oct 2019, Aukland, New Zealand. ⟨10.1007/978-3-030-30793-6_11⟩. ⟨hal-02269830⟩
218 Consultations
35 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More