Improving the recall of decentralised linked data querying through implicit knowledge

Jürgen Umbrich; Aidan Hogan; Axel Polleres

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arXiv · 1109.0181

Improving the recall of decentralised linked data querying through implicit knowledge

Jürgen Umbrich·Aidan Hogan·Axel Polleres

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Abstract

Aside from crawling, indexing, and querying RDF data centrally, Linked Data principles allow for processing SPARQL queries on-the-fly by dereferencing URIs. Proposed link-traversal query approaches for Linked Data have the benefits of up-to-date results and decentralised (i.e., client-side) execution, but operate on incomplete knowledge available in dereferenced documents, thus affecting recall. In this paper, we investigate how implicit knowledge - specifically that found through owl:sameAs and RDFS reasoning - can improve the recall in this setting. We start with an empirical analysis of a large crawl featuring 4 m Linked Data sources and 1.1 g 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 (specifically {\rho}DF) for finding more results, relying on static schema information. We evaluate our methods for live queries over our crawl.

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Jürgen Umbrich, Aidan Hogan, Axel Polleres. 2011-09-01. Improving the recall of decentralised linked data querying through implicit knowledge. https://arxiv.org/abs/1109.0181

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