Beyond Established Knowledge Graphs-Recommending Web Datasets for Data Linking

Mohamed Ben Ellefi 1 Zohra Bellahsene 1 Konstantin Todorov 1 Stefan Dietze 2
1 FADO - Fuzziness, Alignments, Data & Ontologies
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
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.
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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. ⟨10.1007/978-3-319-38791-8_15⟩. ⟨lirmm-01408037⟩

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