  Exploratory information search can challenge users in the formulation of
efficacious search queries. Moreover, complex information spaces, such as those
managed by Geographical Information Systems, can disorient people, making it
difficult to find relevant data. In order to address these issues, we developed
a session-based suggestion model that proposes concepts as a "you might also be
interested in" function, by taking the user's previous queries into account.
Our model can be applied to incrementally generate suggestions in interactive
search. It can be used for query expansion, and in general to guide users in
the exploration of possibly complex spaces of data categories. Our model is
based on a concept co-occurrence graph that describes how frequently concepts
are searched together in search sessions. Starting from an ontological domain
representation, we generated the graph by analyzing the query log of a major
search engine. Moreover, we identified clusters of ontology concepts which
frequently co-occur in the sessions of the log via community detection on the
graph. The evaluation of our model provided satisfactory accuracy results.
