Linkflows: Towards Genuine Semantic Publishing in Science

Abstract

Scientific publishing is the main way in which novel research ideas,evidence,data,and scientific results are communicated,shared and assessed.Currently this process of scientific publishing lacks, most of the time,transparency and machine-interpretable representations.As such,scientific publication is still done in scientific articles,basically long coarse-grained text with complicated structures in natural language that are optimized for human readers and not for automated systems.Moreover, peer reviewing continues to be the main method of quality assessment in science, despite the many and serious flaws emphasized by the scientific community like the lack of transparency,accuracy and efficiency of such a practice.And,as science is rapidly changing and moving more towards a digital environment,with scientific contributions increasing in volume and complexity each day,scientific publishing that still follows this old paradigm of publishing seems to be at odds with the current scientific progress. In this thesis,we propose and evaluate models that tackle how scientific articles and their findings together with their reviews are written and published and how these models can be used in concrete applications that can assist the publishing process for humans and machines alike.First,we introduce a novel fine-grained semantically rich model for reviews in which reviews consist of review comments that contain formal links and semantics from the start and as such are able to make the reviewing process better organized and more accurate.Next,we use this fine-grained reviewing model in conjunction with semantic representations of all the elements of publications and their structure together with all the involved processes,actors and provenance to represent in a unified semantic model the scientific publishing process. Then,we focus on finding a semantic pattern that is able to formally represent the content of high-level scientific findings in a way that can be automatically interpreted.Afterwards,we test these models and applications on a multitude of scientific publication roles such as reviewers, editors,authors and eventually readers in a field study where we prove that not only the scientific publication process (with submissions and final articles) can be represented with formal semantics from the start,but also the whole process in between,including reviews,responses and decisions. Altogether,our research shows that it is possible to bring scientific publishing closer to automated systems and make the elements of publications and their assessments,along with their corresponding processes more machine-interpretable,especially if semantics are captured from the start.The complementary models proposed can be used in the publishing practice and can be adequately used in tools that support the publishing activities and workflows.And,regardless of the challenges encountered along these processes,after reasonable guidance,users are able to use these tools and models with ease despite their novelty, demonstrating that overall our ideas work.This can be a first step in the direction of involving machines more in the research community,where a new way of publishing that considers the Semantic Web principles can help in creating a society where computers can assist the Open Science research community become more efficient and make scientific contributions FAIR: easier to find,more accessible,interoperable and reusable.