SAKey: Scalable Almost Key Discovery in RDF Data

Abstract

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.

Automatic Key Selection for Data Linking

“…The output of step (2) is a set of matched instances, also known as a link set. Finally, step (3) allows to filter out erroneous matches or infer new ones, based on the link set provided in step (2).…”

“…Both [5] and [2] propose a key discovery approach that follows the semantics of a key as defined by OWL. This definition states that two instances are referring to the same real world entity if at least one value per property appearing in a key is equal.…”

“…This definition states that two instances are referring to the same real world entity if at least one value per property appearing in a key is equal. Unlike [5], [2] proposes a method that scales on large datasets, taking also into account errors or duplicates in the data. In [6] and [4], the authors propose an alternative definition for the keys that is valid when the data are locally complete.…”

“…Only mapped properties, appearing in M , will be contained in the discovered keys. For this step, existing key discovery tools such as SAKey [2] or ROCKER [4] can be used to obtain keys for a given class C.…”

“…Indeed, experts may not know all the specificities of a dataset leading to overlook certain keys or even introduce erroneous ones. For this reason, several automatic key discovery approaches have been already proposed in the context of the Semantic Web [2,3,4,5,6].…”