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An increasing number of scientific publications are created in open andtransparent peer review models: a submission is published first, and thenreviewers are invited, or a submission is reviewed in a closed environment butthen these reviews are published with the final article, or combinations ofthese. Reasons for open peer review include giving better credit to reviewersand enabling readers to better appraise the quality of a publication. In mostcases, the full, unstructured text of an open review is published next to thefull, unstructured text of the article reviewed. This approach prevents humanreaders from getting a quick impression of the quality of parts of an article,and it does not easily support secondary exploitation, e.g., for scientometricson reviews. While document formats have been proposed for publishing structuredarticles including reviews, integrated tool support for entire open peer reviewworkflows resulting in such documents is still scarce. We present AR-Annotator,the Automatic Article and Review Annotator which employs a semantic informationmodel of an article and its reviews, using semantic markup and uniqueidentifiers for all entities of interest. The fine-grained article structure isnot only exposed to authors and reviewers but also preserved in the publishedversion. We publish articles and their reviews in a Linked Data representationand thus maximize their reusability by third-party applications. We demonstratethis reusability by running quality-related queries against the structuredrepresentation of articles and their reviews.

Automatically Annotating Articles Towards Opening and Reusing Transparent Peer Reviews