  The task of expert finding has been getting increasing attention in
information retrieval literature. However, the current state-of-the-art is
still lacking in principled approaches for combining different sources of
evidence. This paper explores the usage of unsupervised rank aggregation
methods as a principled approach for combining multiple estimators of
expertise, derived from the textual contents, from the graph-structure of the
citation patterns for the community of experts, and from profile information
about the experts. We specifically experimented two unsupervised rank
aggregation approaches well known in the information retrieval literature,
namely CombSUM and CombMNZ. Experiments made over a dataset of academic
publications for the area of Computer Science attest for the adequacy of these
methods.
