  The use of science to understand its own structure is becoming popular, but
understanding the organization of knowledge areas is still limited because some
patterns are only discoverable with proper computational treatment of
large-scale datasets. In this paper, we introduce a network-based methodology
combined with text analytics to construct the taxonomy of science fields. The
methodology is illustrated with application to two topics: complex networks
(CN) and photonic crystals (PC). We built citation networks using data from the
Web of Science and used a community detection algorithm for partitioning to
obtain science maps of the fields considered. We also created an importance
index for text analytics in order to obtain keywords that define the
communities. A dendrogram of the relatedness among the subtopics was also
obtained. Among the interesting patterns that emerged from the analysis, we
highlight the identification of two well-defined communities in PC area, which
is consistent with the known existence of two distinct communities of
researchers in the area: telecommunication engineers and physicists. With the
methodology, it was also possible to assess the interdisciplinary and time
evolution of subtopics defined by the keywords. The automatic tools described
here are potentially useful not only to provide an overview of scientific areas
but also to assist scientists in performing systematic research on a specific
topic.
