Binary RDF representation for publication and exchange (HDT)

  • Authors:

  • Javier D. FernáNdez;Miguel A. MartíNez-Prieto;Claudio GutiéRrez;Axel Polleres;Mario Arias

  • Affiliations:

  • DataWeb Research, Department of Computer Science, University of Valladolid, E.T.S. de Ingeniería Informática, Campus Miguel Delibes, 47011 Valladolid, Spain;DataWeb Research, Department of Computer Science, University of Valladolid, E.T.S. de Ingeniería Informática, Campus Miguel Delibes, 47011 Valladolid, Spain and Department of Computer Sc ...;Department of Computer Science, University of Chile, Avenida Blanco Encalada 2120, 837-0459 Santiago, Chile;Digital Enterprise Research Institute, National University of Ireland, Galway, IDA Business Park, Lower Dangan, Galway, Ireland and Siemens AG Österreich, Siemensstrasse 90, 1210 Vienna, Aust ...;DataWeb Research, Department of Computer Science, University of Valladolid, E.T.S. de Ingeniería Informática, Campus Miguel Delibes, 47011 Valladolid, Spain and Siemens AG Österreic ...

  • Venue:
  • Web Semantics: Science, Services and Agents on the World Wide Web
  • Year:
  • 2013

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Abstract

The current Web of Data is producing increasingly large RDF datasets. Massive publication efforts of RDF data driven by initiatives like the Linked Open Data movement, and the need to exchange large datasets has unveiled the drawbacks of traditional RDF representations, inspired and designed by a document-centric and human-readable Web. Among the main problems are high levels of verbosity/redundancy and weak machine-processable capabilities in the description of these datasets. This scenario calls for efficient formats for publication and exchange. This article presents a binary RDF representation addressing these issues. Based on a set of metrics that characterizes the skewed structure of real-world RDF data, we develop a proposal of an RDF representation that modularly partitions and efficiently represents three components of RDF datasets: Header information, a Dictionary, and the actual Triples structure (thus called HDT). Our experimental evaluation shows that datasets in HDT format can be compacted by more than fifteen times as compared to current naive representations, improving both parsing and processing while keeping a consistent publication scheme. Specific compression techniques over HDT further improve these compression rates and prove to outperform existing compression solutions for efficient RDF exchange.