Beyond Established Knowledge Graphs-Recommending Web Datasets for Data Linking

Abstract : With the explosive growth of the Web of Data in terms of size and complexity, identifying suitable datasets to be linked, has become a challenging problem for data publishers. To understand the nature of the content of specific datasets, we adopt the notion of dataset profiles, where datasets are characterized through a set of topic annotations. In this paper, we adopt a collaborative filtering-like recommendation approach, which exploits both existing dataset profiles, as well as traditional dataset connectivity measures, in order to link arbitrary, non-profiled datasets into a global dataset-topic-graph. Our experiments, applied to all available Linked Datasets in the Linked Open Data (LOD) cloud, show an average recall of up to 81%, which translates to an average reduction of the size of the original candidate dataset search space to up to 86%. An additional contribution of this work is the provision of benchmarks for dataset interlinking recommendation systems.
Type de document :
Communication dans un congrès
ICWE: International Conference on Web Engineering, Jun 2016, Lugano, Switzerland. 16th International Conference on Web Engineering, 2016, 〈http://icwe2016.inf.usi.ch〉. 〈10.1007/978-3-319-38791-8_15〉
Liste complète des métadonnées

Littérature citée [18 références]  Voir  Masquer  Télécharger

https://hal-lirmm.ccsd.cnrs.fr/lirmm-01408037
Contributeur : Mohamed Ben Ellefi <>
Soumis le : lundi 5 décembre 2016 - 10:58:23
Dernière modification le : jeudi 24 mai 2018 - 15:59:21
Document(s) archivé(s) le : mardi 21 mars 2017 - 01:06:28

Fichier

ICWECamReady.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

Mohamed Ben Ellefi, Zohra Bellahsene, Konstantin Todorov, Stefan Dietze. Beyond Established Knowledge Graphs-Recommending Web Datasets for Data Linking. ICWE: International Conference on Web Engineering, Jun 2016, Lugano, Switzerland. 16th International Conference on Web Engineering, 2016, 〈http://icwe2016.inf.usi.ch〉. 〈10.1007/978-3-319-38791-8_15〉. 〈lirmm-01408037〉

Partager

Métriques

Consultations de la notice

69

Téléchargements de fichiers

134