SAKey: Scalable Almost Key Discovery in RDF Data

Danai Symeonidou 1, 2 Vincent Armant 3 Nathalie Pernelle 1, 2 Fatiha Saïs 1, 2
1 LRI - Laboratoire de Recherche en Informatique
2 LaHDAK - LRI - Données et Connaissances Massives et Hétérogènes (LRI)
LRI - Laboratoire de Recherche en Informatique
3 INSIGHT - Insight Centre for Data Analytics (National University of Ireland Galway (NUIG))
Abstract : Exploiting identity links among RDF resources allows applications to efficiently integrate data. Keys can be very useful to discover these identity links. A set of properties is considered as a key when its values uniquely identify resources. However, these keys are usually not available. The approaches that attempt to automatically discover keys can easily be overwhelmed by the size of the data and require clean data. We present SAKey, an approach that discovers keys in RDF data in an efficient way. To prune the search space, SAKey exploits characteristics of the data that are dynamically detected during the process. Furthermore , our approach can discover keys in datasets where erroneous data or duplicates exist (i.e., almost keys). The approach has been evaluated on different synthetic and real datasets. The results show both the relevance of almost keys and the efficiency of discovering them.
Keywords : Keys Identity Links Data Linking RDF OWL2
Type de document :
Communication dans un congrès
Springer Verlag. In proceedings of the 13th International Semantic Web Conference, ISWC 2014, Oct 2014, Riva del Garda, Italy. Lecture Notes in Computer Science (8796), pp.33--49, 2014, 〈10.1007/978-3-319-11964-9_3〉
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Danai Symeonidou, Vincent Armant, Nathalie Pernelle, Fatiha Saïs. SAKey: Scalable Almost Key Discovery in RDF Data. Springer Verlag. In proceedings of the 13th International Semantic Web Conference, ISWC 2014, Oct 2014, Riva del Garda, Italy. Lecture Notes in Computer Science (8796), pp.33--49, 2014, 〈10.1007/978-3-319-11964-9_3〉. 〈hal-01275954〉

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