This paper aims to propose an approach for the semantification of scientific papers so as to save the time of researchers in obtaining access to knowledge of their domain, comparing research contributions and getting novel insights.
The approach proposed in this paper consists of extracting key insights from scientific papers leveraging neural models and organizing them using a symbolic AI model which is a scholarly knowledge graph (KG). To validate this approach a research KG is implemented.
This research KG aims to semantify papers published by the SemTab@ISWC (Semantic Web Challenge on Tabular Data to KG Matching @ International Semantic Web Conference) venue. This research KG is composed of more than 650 instances. The authors demonstrate the utility of the KG obtained by using it to answer SPARQL [SPARQL Protocol and Resource Description Framework (RDF) Query Language] queries about several questions a researcher may have on semantic table annotation.
This is an original work to propose a neuro-symbolic (NeSy) approach which leverages connectionist models and a symbolic model for the semantification of scientific papers.
