Semantic Publishing Challenge -- Assessing the Quality of Scientific Output
This work addresses the need for improved quality assessment in scientific publishing, but it appears incremental as it builds on existing challenges and focuses on specific datasets.
The paper tackled the problem of generating richer semantic data from scholarly publications to assess their quality, focusing on workshop proceedings and journal articles' citation networks, and demonstrated the use of Semantic Web technologies for this purpose.
Linked Open Datasets about scholarly publications enable the development and integration of sophisticated end-user services; however, richer datasets are still needed. The first goal of this Challenge was to investigate novel approaches to obtain such semantic data. In particular, we were seeking methods and tools to extract information from scholarly publications, to publish it as LOD, and to use queries over this LOD to assess quality. This year we focused on the quality of workshop proceedings, and of journal articles w.r.t. their citation network. A third, open task, asked to showcase how such semantic data could be exploited and how Semantic Web technologies could help in this emerging context.