Bringing head closer to the tail with entity linking

With the creation and rapid development of knowledge bases, it has become easier to understand the underlying semantics of unstructured text (short or long) on the web. In this work we especially look at the impact of entity linking on search logs. Search queries follow a Zipfian distribution wherein other than few popular queries (emph{head queries}), a significant percentage of queries (emph{tail queries}) occur rarely. Given a search log, there is sufficient data to analyze head queries but insufficient data (low frequency, limited clicks) to draw any conclusions about tail queries. In this work we focus on quantifying the extent of overlap between long tail and head queries by means of entity linking. We specifically analyze the frequency distribution of entities in head and tail queries. Our analysis shows that by means of entity linking, we can indeed bridge the gap between the head and tail.