Improving the recall of decentralised linked data querying through implicit knowledge

Aside from crawling, indexing, and querying RDF data centrally, Linked Data principlesallow for processing SPARQL queries on-the-fly by dereferencing URIs. Proposed link-traversalquery 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 foundthrough owl:sameAs and RDFS reasoning – can improve the recall in this setting. We start withan 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 theimprovement in recall given by considering rdfs:seeAlso links as previous proposals did. Wefurther propose and measure the impact of additionally considering (3) owl:sameAs links, and(4) applying lightweight RDFS reasoning (specifically ρDF) for finding more results, relying onstatic schema information. We evaluate our methods for live queries over our crawl.