  This paper is about a better understanding on the structure and dynamics of
science and the usage of these insights for compensating the typical problems
that arises in metadata-driven Digital Libraries. Three science model driven
retrieval services are presented: co-word analysis based query expansion,
re-ranking via Bradfordizing and author centrality. The services are evaluated
with relevance assessments from which two important implications emerge: (1)
precision values of the retrieval service are the same or better than the
tf-idf retrieval baseline and (2) each service retrieved a disjoint set of
documents. The different services each favor quite other - but still relevant -
documents than pure term-frequency based rankings. The proposed models and
derived retrieval services therefore open up new viewpoints on the scientific
knowledge space and provide an alternative framework to structure scholarly
information systems.
