  Query suggestion refers to the task of suggesting relevant and related
queries to a search engine user to help in query formulation process and to
expedite information retrieval with minimum amount of effort. It is highly
useful in situations where the search requirements are not well understood and
hence it has been widely adopted by search engines to guide users' search
activity. For news websites, user queries have a time sensitive nature inherent
in them. When some new event happens, there is a sudden burst in queries
related to that event and such queries are sustained over a period of time
before fading away with that event. In addition to this temporal aspect of
search queries fired at news websites, they have an addition distinct quality,
i.e., they are intended to get event related information majority of the times.
Existing work on generating query suggestions involves analyzing query logs to
suggest queries which are relevant and related to the search intent of the
user. But in case of news websites, when there is a sudden burst in information
related to a particular event, there are not enough search queries fired by
other users which leads to lack of click data, and hence giving query
suggestions related to some old event or even some irrelevant suggestions
altogether. Another problem with query logs in the context of online news is
that, they mostly contain queries related to popular events and hence fail to
capture less popular events or events which got overshadowed by some other more
sensational event. We propose a novel approach to generate event-centric query
suggestions using metadata of news articles published by news media. We
compared our proposed framework with existing state of the art query suggestion
mechanisms provided by Google News, Bing News, Google Search and Bing Search on
various parameters.
