Semantic Publishing Challenge -- Assessing the Quality of Scientific Output

TL;DR

This paper discusses the Semantic Publishing Challenge, which aims to develop methods for extracting, publishing, and querying semantic data from scholarly publications to assess their quality, focusing on workshop proceedings and journal articles.

Contribution

It introduces novel approaches and tools for semantic data extraction from publications and demonstrates their application in assessing publication quality using Semantic Web technologies.

Findings

Methods for extracting semantic data from publications

Tools for publishing scholarly data as Linked Open Data

Queries over LOD to evaluate publication quality

Abstract

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.