  A science map of topics is a visualization that shows topics identified
algorithmically based on the bibliographic metadata of scientific publications.
In practice not all topics are well represented in a science map. We analyzed
how effectively different topics are represented in science maps created by
clustering biomedical publications. To achieve this, we investigated which
topic categories, obtained from MeSH terms, are better represented in science
maps based on citation or text similarity networks. To evaluate the clustering
effectiveness of topics, we determined the extent to which documents belonging
to the same topic are grouped together in the same cluster. We found that the
best and worst represented topic categories are the same for citation and text
similarity networks. The best represented topic categories are diseases,
psychology, anatomy, organisms and the techniques and equipment used for
diagnostics and therapy, while the worst represented topic categories are
natural science fields, geographical entities, information sciences and health
care and occupations. Furthermore, for the diseases and organisms topic
categories and for science maps with smaller clusters, we found that topics
tend to be better represented in citation similarity networks than in text
similarity networks.
