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SAKey: Scalable Almost Key discovery in RDF data


Danai Symeonidou, Vincent Armant, Nathalie Pernelle, Sais Fatiha

Publication Type: 
Refereed Conference Meeting Proceeding
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.
Conference Name: 
ISWC 2014
ISWC 2014, October 19th -23rd Riva del Garda, Trento, Italy
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Conference Location: 
National University of Ireland, Cork (UCC)
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