  Users who need several queries before finding what they need can benefit from
an automatic search assistant that provides feedback on their query
modification strategies. We present a method to learn from a search log which
types of query modifications have and have not been effective in the past. The
method analyses query modifications along two dimensions: a traditional
term-based dimension and a semantic dimension, for which queries are enriches
with linked data entities. Applying the method to the search logs of two search
engines, we identify six opportunities for a query modification assistant to
improve search: modification strategies that are commonly used, but that often
do not lead to satisfactory results.
