  An increasing number of scientific publications are created in open and
transparent peer review models: a submission is published first, and then
reviewers are invited, or a submission is reviewed in a closed environment but
then these reviews are published with the final article, or combinations of
these. Reasons for open peer review include giving better credit to reviewers
and enabling readers to better appraise the quality of a publication. In most
cases, the full, unstructured text of an open review is published next to the
full, unstructured text of the article reviewed. This approach prevents human
readers from getting a quick impression of the quality of parts of an article,
and it does not easily support secondary exploitation, e.g., for scientometrics
on reviews. While document formats have been proposed for publishing structured
articles including reviews, integrated tool support for entire open peer review
workflows resulting in such documents is still scarce. We present AR-Annotator,
the Automatic Article and Review Annotator which employs a semantic information
model of an article and its reviews, using semantic markup and unique
identifiers for all entities of interest. The fine-grained article structure is
not only exposed to authors and reviewers but also preserved in the published
version. We publish articles and their reviews in a Linked Data representation
and thus maximize their reusability by third-party applications. We demonstrate
this reusability by running quality-related queries against the structured
representation of articles and their reviews.
