Exploratory Knowledge Discovery over Web of Data

Mehwish Alam 1 Aleksey Buzmakov 2 Amedeo Napoli 3
3 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : With an increased interest in machine processable data and with the progress of semantic technologies, many datasets are now published in the form of RDF triples for constituting the so-called Web of Data. Data can be queried using SPARQL but there are still needs for integrating, classifying and exploring the data for data analysis and knowledge discovery purposes. This research work proposes a new approach based on Formal Concept Analysis and Pattern Structures for building a pattern concept lattice from a set of RDF triples. This lattice can be used for data exploration and in particular visualized thanks to an adapted tool. The specific pattern structure introduced for RDF data allows to make a bridge with other studies on the use of structured attribute sets when building concept lattices. Our approach is experimentally validated on the classification of RDF data showing the efficiency of the underlying algorithms.
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Mehwish Alam, Aleksey Buzmakov, Amedeo Napoli. Exploratory Knowledge Discovery over Web of Data. Discrete Applied Mathematics, Elsevier, 2018, 249, pp.2-17. ⟨10.1016/j.dam.2018.03.041⟩. ⟨hal-01673439⟩

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