  We provide an up-to-date view on the knowledge management system ScienceWISE
(SW) and address issues related to the automatic assignment of articles to
research topics. So far, SW has been proven to be an effective platform for
managing large volumes of technical articles by means of ontological
concept-based browsing. However, as the publication of research articles
accelerates, the expressivity and the richness of the SW ontology turns into a
double-edged sword: a more fine-grained characterization of articles is
possible, but at the cost of introducing more spurious relations among them. In
this context, the challenge of continuously recommending relevant articles to
users lies in tackling a network partitioning problem, where nodes represent
articles and co-occurring concepts create edges between them. In this paper, we
discuss the three research directions we have taken for solving this issue: i)
the identification of generic concepts to reinforce inter-article similarities;
ii) the adoption of a bipartite network representation to improve scalability;
iii) the design of a clustering algorithm to identify concepts for
cross-disciplinary articles and obtain fine-grained topics for all articles.
