LODStats - An Extensible Framework for High-performance Dataset Analytics

Presented at: 18th International Conference on Knowledge Engineering and Knowledge Management (EKAW2012)

by Sören Auer, Jan Demter, Michael Martin, Jens Lehmann

One of the major obstacles for a wider usage of Web Data is the difficulty to obtain a clear picture of the available datasets. In order to reuse, link, revise or query a dataset published on the Web it is important to know the structure, coverage and coherence of the data. In order to obtain such information we developed LODStats - a statement-stream-based approach for gathering comprehensive statistics about datasets adhering to the Resource Description Framework (RDF). LODStats is based on the declarative description of statistical dataset characteristics. Its main advantages over other approaches are a smaller memory footprint and significantly better performance and scalability. We integrated LODStats into the CKAN dataset metadata registry and obtained a comprehensive picture of the current state of the Data Web.


Resource URI on the dog food server: http://data.semanticweb.org/conference/ekaw/2012/paper/inuse/54


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