Contextual Itemset Mining in DBpedia

Julien Rabatel 1, 2 Madalina Croitoru 3 Dino Ienco 2, 4 Pascal Poncelet 2
1 Itesoft R&D
2 ADVANSE - ADVanced Analytics for data SciencE
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
3 GRAPHIK - Graphs for Inferences on Knowledge
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
4 UMR TETIS - Territoires, Environnement, Télédétection et Information Spatiale
Abstract : In this paper we show the potential of contextual itemset mining in the context of Linked Open Data. Contextual itemset mining extracts frequent associations among items considering background information. In the case of Linked Open Data, the background information is represented by an Ontology defined over the data. Each resulting itemset is specific to a particular context and contexts can be related each others following the ontological structure. We use contextual mining on DBpedia data and show how the use of contextual information can refine the itemsets obtained by the knowledge discovery process.
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Submitted on : Thursday, October 23, 2014 - 1:57:19 PM
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Julien Rabatel, Madalina Croitoru, Dino Ienco, Pascal Poncelet. Contextual Itemset Mining in DBpedia. LD4KD: Linked Data for Knowledge Discovery, Sep 2014, Nancy, France. http://ceur-ws.org/Vol-1232/. ⟨lirmm-01076887⟩

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