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
3 GRAPHIK - Graphs for Inferences on Knowledge
4 UMR TETIS - Territoires, Environnement, Télédétection et Information Spatiale

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.

Mots clés: DATA HANDLING

Mots clés: BACKGROUND INFORMATION; ITEM SETS; ITEMSET; ITEMSET MINING; KNOWLEDGE DISCOVERY PROCESS; LINKED OPEN DATUM; LEARNING SYSTEMS

Mots clés: CONTEXTUAL INFORMATION; ONTOLOGICAL STRUCTURES

Mots clés: ARTIFICIAL INTELLIGENCE

Domaines: Recherche d'information [cs.IR]; Intelligence artificielle [cs.AI]; Web; Base de données [cs.DB]

Julien Rabatel, Madalina Croitoru, Dino Ienco, Pascal Poncelet. Contextual Itemset Mining in DBpedia. LD4KD: Linked Data for Knowledge Discovery, Sep 2014, Nancy, France. pp.27-36. ⟨lirmm-01076887⟩