  Document clustering is generally the first step for topic identification.
Since many clustering methods operate on the similarities between documents, it
is important to build representations of these documents which keep their
semantics as much as possible and are also suitable for efficient similarity
calculation. The metadata of articles in the Astro dataset contribute to a
semantic matrix, which uses a vector space to capture the semantics of entities
derived from these articles and consequently supports the contextual
exploration of these entities in LittleAriadne. However, this semantic matrix
does not allow to calculate similarities between articles directly. In this
paper, we will describe in detail how we build a semantic representation for an
article from the entities that are associated with it. Base on such semantic
representations of articles, we apply two standard clustering methods, K-Means
and the Louvain community detection algorithm, which leads to our two
clustering solutions labelled as OCLC-31 (standing for K-Means) and
OCLC-Louvain (standing for Louvain). In this paper, we will give the
implementation details and a basic comparison with other clustering solutions
that are reported in this special issue.
