  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.
