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- 3360901.3364434 type ConferencePaper assertion.
- author-list _1 0000-0002-2241-8585 assertion.
- author-list _2 0000-0002-1267-0234 assertion.
- author-list _3 0000-0002-3357-9130 assertion.
- 0000-0002-1267-0234 name "Tobias Kuhn" assertion.
- 0000-0002-3357-9130 name "Davide Ceolin" assertion.
- 008xxew50 name "Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands" assertion.
- 0000-0002-2241-8585 name "Cristina-Iulia Bucur" assertion.
- 00x7ekv49 name "Centrum Wiskunde & Informatica, Amsterdam, Netherlands" assertion.
- 3360901.3364434 title "Peer Reviewing Revisited: Assessing Research with Interlinked Semantic Comments" assertion.
- kcap-2019 title "Proceedings of the 10th International Conference on Knowledge Capture (K-CAP 2019)" assertion.
- 3360901.3364434 date "2019-09-23" assertion.
- 3360901.3364434 isPartOf kcap-2019 assertion.
- 3360901.3364434 abstract "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." assertion.
- 0000-0002-1267-0234 affiliation 008xxew50 assertion.
- 0000-0002-3357-9130 affiliation 00x7ekv49 assertion.
- 0000-0002-2241-8585 affiliation 008xxew50 assertion.
- 3360901.3364434 authorList author-list assertion.
- 3360901.3364434 pageEnd "187" assertion.
- 3360901.3364434 pageStart "179" assertion.