The Web of Data is growing in popularity and dimension, and named entity exploitation is gaining importance in many research elds. 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 assess 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.
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