SampLD

The Linked Data cloud has grown to become the largest knowledge base ever constructed. Its size is now turning into a major bottleneck in many applications. In order to facilitate access to this enormous amount of structured information, this paper proposes an automatic sampling method targeted at maximizing answer coverage in applications using SPARQL querying. We empirically show that the relevance of triples for sampling (a semantic notion) is influenced by the topology of the graph (purely structural), and can be determined without prior knowledge of the queries. Usage of state-of-the-art high-performance methods allowed this analysis to be performed on a previously unprecedented scale. Experiments show a significantly higher recall of topology based sampling methods over random and naive baseline approaches