Article,
Finding Experts on the Semantic Desktop
(2008)
Abstract
Expert retrieval has attracted deep attention because of the
huge economical impact it can have on enterprises. The classical dataset
on which to perform this task is company intranet (i.e., personal pages,
e-mails, documents). We propose a new system for nding experts in the
user's desktop content. Looking at private documents and e-mails of the
user, the system builds expert pro les for all the people named in the
desktop. This allows the search system to focus on the user's topics of
interest thus generating satisfactory results on topics well represented on
the desktop. We show, with an arti cial test collection, how the desktop
content is appropriate for nding experts on the topic the user is
interested in.
