The Web of Data is growing in popularity and dimension,
and named entity exploitation is gaining importance in many research
fields. In this paper, we explore the use of entities that can be extracted
from a query log to enhance query recommendation. In particular, we
extend a state-of-the-art recommendation algorithm to take into account
the semantic information associated with submitted queries. Our novel
method generates highly related and diversified suggestions that we as-
sess by means of a new evaluation technique. The manually annotated
dataset used for performance comparisons has been made available to
the research community to favor the repeatability of experiments