  The advances of the Linked Open Data (LOD) initiative are giving rise to a
more structured Web of data. Indeed, a few datasets act as hubs (e.g., DBpedia)
connecting many other datasets. They also made possible new Web services for
entity detection inside plain text (e.g., DBpedia Spotlight), thus allowing for
new applications that can benefit from a combination of the Web of documents
and the Web of data. To ease the emergence of these new applications, we
propose a query-biased algorithm (LDRANK) for the ranking of web of data
resources with associated textual data. Our algorithm combines link analysis
with dimensionality reduction. We use crowdsourcing for building a publicly
available and reusable dataset for the evaluation of query-biased ranking of
Web of data resources detected in Web pages. We show that, on this dataset,
LDRANK outperforms the state of the art. Finally, we use this algorithm for the
construction of semantic snippets of which we evaluate the usefulness with a
crowdsourcing-based approach.
