  We will report on the participation of GESIS at the first CHiC workshop
(Cultural Heritage in CLEF). Being held for the first time, no prior experience
with the new data set, a document dump of Europeana with ca. 23 million
documents, exists. The most prominent issues that arose from pretests with this
test collection were the very unspecific topics and sparse document
representations. Only half of the topics (26/50) contained a description and
the titles were usually short with just around two words. Therefore we focused
on three different term suggestion and query expansion mechanisms to surpass
the sparse topical description. We used two methods that build on concept
extraction from Wikipedia and on a method that applied co-occurrence statistics
on the available Europeana corpus. In the following paper we will present the
approaches and preliminary results from their assessments.
