Abstract
This paper is about a better understanding of 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.
Users
Please
log in to take part in the discussion (add own reviews or comments).