Improving the recall of live linked data querying through reasoning

  • Authors:

  • Jürgen Umbrich;Aidan Hogan;Axel Polleres;Stefan Decker

  • Affiliations:

  • Digital Enterprise Research Institute, National University of Ireland, Galway, Ireland;Digital Enterprise Research Institute, National University of Ireland, Galway, Ireland;Siemens AG Österreich, Vienna, Austria;Digital Enterprise Research Institute, National University of Ireland, Galway, Ireland

  • Venue:
  • RR'12 Proceedings of the 6th international conference on Web Reasoning and Rule Systems
  • Year:
  • 2012

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Abstract

Linked Data principles allow for processing SPARQL queries on-the-fly by dereferencing URIs. Link-traversal query approaches for Linked Data have the benefit of up-to-date results and decentralised execution, but operate only on explicit data from dereferenced documents, affecting recall. In this paper, we show how inferable knowledge--specifically that found through owl:sameAs and RDFS reasoning--can improve recall in this setting. We first analyse a corpus featuring 7 million Linked Data sources and 2.1 billion quadruples: we (1) measure expected recall by only considering dereferenceable information, (2) measure the improvement in recall given by considering rdfs:seeAlso links as previous proposals did. We further propose and measure the impact of additionally considering (3) owl:sameAs links, and (4) applying lightweight RDFS reasoning for finding more results, relying on static schema information. We evaluate different configurations for live queries covering different shapes and domains, generated from random walks over our corpus.