  The continuous growth of scientific literature brings innovations and, at the
same time, raises new challenges. One of them is related to the fact that its
analysis has become difficult due to the high volume of published papers for
which manual effort for annotations and management is required. Novel
technological infrastructures are needed to help researchers, research policy
makers, and companies to time-efficiently browse, analyse, and forecast
scientific research. Knowledge graphs i.e., large networks of entities and
relationships, have proved to be effective solution in this space. Scientific
knowledge graphs focus on the scholarly domain and typically contain metadata
describing research publications such as authors, venues, organizations,
research topics, and citations. However, the current generation of knowledge
graphs lacks of an explicit representation of the knowledge presented in the
research papers. As such, in this paper, we present a new architecture that
takes advantage of Natural Language Processing and Machine Learning methods for
extracting entities and relationships from research publications and integrates
them in a large-scale knowledge graph. Within this research work, we i) tackle
the challenge of knowledge extraction by employing several state-of-the-art
Natural Language Processing and Text Mining tools, ii) describe an approach for
integrating entities and relationships generated by these tools, iii) show the
advantage of such an hybrid system over alternative approaches, and vi) as a
chosen use case, we generated a scientific knowledge graph including 109,105
triples, extracted from 26,827 abstracts of papers within the Semantic Web
domain. As our approach is general and can be applied to any domain, we expect
that it can facilitate the management, analysis, dissemination, and processing
of scientific knowledge.
