  Scientific publishing seems to be at a turning point. Its paradigm has stayed
basically the same for 300 years but is now challenged by the increasing volume
of articles that makes it very hard for scientists to stay up to date in their
respective fields. In fact, many have pointed out serious flaws of current
scientific publishing practices, including the lack of accuracy and efficiency
of the reviewing process. To address some of these problems, we apply here the
general principles of the Web and the Semantic Web to scientific publishing,
focusing on the reviewing process. We want to determine if a fine-grained model
of the scientific publishing workflow can help us make the reviewing processes
better organized and more accurate, by ensuring that review comments are
created with formal links and semantics from the start. Our contributions
include a novel model called Linkflows that allows for such detailed and
semantically rich representations of reviews and the reviewing processes. We
evaluate our approach on a manually curated dataset from several recent
Computer Science journals and conferences that come with open peer reviews. We
gathered ground-truth data by contacting the original reviewers and asking them
to categorize their own review comments according to our model. Comparing this
ground truth to answers provided by model experts, peers, and automated
techniques confirms that our approach of formally capturing the reviewers'
intentions from the start prevents substantial discrepancies compared to when
this information is later extracted from the plain-text comments. In general,
our analysis shows that our model is well understood and easy to apply, and it
revealed the semantic properties of such review comments.
