The way how research is communicated using text publications has not changed much over the past decades. We have the vision that ultimately researchers will work on a common structured knowledge base comprising comprehens- ive semantic and machine-comprehensible descriptions of their research, thus making research contributions more transparent and comparable. We present the SemSur ontology for semantically capturing the information commonly found in survey and review articles. SemSur is able to represent scientific results and to publish them in a com- prehensive knowledge graph, which provides an efficient overview of a research field and compare research findings with related works in a structured way saving a significant amount of time and effort. The new release of SemSur covers more domains, defines better alignment with external ontologies and rules for eliciting implicit knowledge. We discuss possible applications and present an evaluation of our approach with the retrospective, exemplary semantific- ation of a survey. We demonstrate the utility of the SemSur ontology to answer queries about the different research contributions covered by the survey. SemSur is currently used and maintained in OpenResearch.org.
University of Bonn
https://www.informatik.uni-bonn.de/de
University of Bonn
https://www.informatik.uni-bonn.de/de
Director
Leibniz Information Center for Science & Technology
https://www.tib.eu/en/
Before being appointed Director of TIB, Professor Dr.
National Chung Hsing University, Taiwan, University of Bonn & Fraunhofer IAIS
www.cs.uni-bonn.de
