  The purpose of the paper is to propose models to reduce the semantic
complexity in heterogeneous DLs. The aim is to introduce value-added services
(treatment of term vagueness and document re-ranking) that gain a certain
quality in DLs if they are combined with heterogeneity components established
in the project "Competence Center Modeling and Treatment of Semantic
Heterogeneity". Empirical observations show that freely formulated user terms
and terms from controlled vocabularies are often not the same or match just by
coincidence. Therefore, a value-added service will be developed which rephrases
the natural language searcher terms into suggestions from the controlled
vocabulary, the Search Term Recommender (STR). Two methods, which are derived
from scientometrics and network analysis, will be implemented with the
objective to re-rank result sets by the following structural properties: the
ranking of the results by core journals (so-called Bradfordizing) and ranking
by centrality of authors in co-authorship networks.
