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Résultat majeur : ISWC'14: SAKEY: SCALABLE ALMOST KEY DISCOVERY IN RDF DATA
ISWC'14: SAKEY: SCALABLE ALMOST KEY DISCOVERY IN RDF DATA
19 octobre 2014

Danai Symeonidou, Vincent Armant, Nathalie Pernelle, Fatiha Saïs
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. Further- more, 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.



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