  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 will benefit from a combination of the web of documents
and the web of data. To ease the emergence of these new use-cases, we propose a
query-biased algorithm for the ranking of entities detected inside a web page.
Our algorithm combine link analysis with dimensionality reduction. We use
crowdsourcing for building a publicly available and reusable dataset on which
we compare our algorithm to the state of the art. Finally, we use this
algorithm for the construction of semantic snippets for which we evaluate the
usability and the usefulness with a crowdsourcing-based approach.
