Like most tasks assessed at retrieval evaluation platforms
such as TREC, NTCIR, CLEF, and INEX, the expert finding
task is an abstraction of a real task. The abstractions are important
when trying to set up experiments that will lead to
stable and re-usable test sets [Voorhees and Harman, 2005].
But when people search for expertise, they are often looking for experts, but not in isolation—the desired output should be
more than a ranked list of person names [Hawking, 2004].
Context and evidence are needed to help users of expertise
finding systems decide whom to contact when seeking expertise
in some area. E.g., given an expert whose name is
returned in response to a query, what are her areas of expertise?
Who does she work with? What are her contact details?
Is she well-connected, just in case she is not able to help us
herself? The main aim of this paper is to introduce the task of determining
an expert’s profile—i.e., a concise description of
the areas in which she is an expert plus a description of her
collaboration environment—, and to devise and assess algorithms
that address this profiling task. To make matters more
concrete, let us look at an expert finding system that is currently
being developed; it started out as “a ranked list of person
names” system and is now evolving so as to include the
type of context and evidence discussed above. Figure 1 provides
a screen dump. In Figure 1 we see the information dis