  In this paper we are using the poset representation to describe the complex
answers given by IR systems after a clustering and ranking processes. The
answers considered may be given by cartographical representations or by
thematic sub-lists of documents. The poset representation, with the graph
theory and the relational representation opens many perspectives in the
definition of new similarity measures capable of taking into account both the
clustering and ranking processes. We present a general method for constructing
new similarity measures and give several examples. These measures can be used
for semi-ordered partitions; moreover, in the comparison of two sets of
answers, the corresponding similarity indicator is an increasing function of
the ranks of presentation of common answers.
