In a library there is a lot of naming: Marking documents with descriptive names and assigning documents to named categories. This necessary naming activity is, however, the site of tensions between the procedural need for stable marks and the inherent multiplicity and instability of linguistic expressions used to represent topics. Here we provide a brief introduction to the issues,

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 tfidf 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. 1

The paper proposes three different kinds of science models as value-added services that are integrated in the retrieval process to enhance retrieval quality. The paper discusses the approaches Search Term Recommendation, Bradfordizing and Author Centrality on a general level and addresses implementation issues of the models within a real-life retrieval environment.