In this paper, we present a preliminary approach that uses a set of NLP and Deep Learning methods for extracting entities and relationships from research publications and then integrates them in a Knowledge Graph. More specifically, 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, and iii) analyse an automatically generated Knowledge Graph including 10,425 entities and 25,655 relationships in the field of Semantic Web
A scientific paper can be divided into two major constructs which are Metadata and Full-body text. M...
We describe an annotation initiative to capture the scholarly contributions in natural language proc...
Natural language text, from messages on social media to articles in newspapers, constitutes a signif...
Knowledge graphs (KG) are large network of entities and relationships, tipically expressed as RDF tr...
In this paper, we present a preliminary approach that uses a set of NLP and Deep Learning methods fo...
Knowledge graphs (KG) are large networks of entities and relationships, typically expressed as RDF t...
The continuous growth of scientific literature brings innovations and, at the same time, raises new ...
Science communication has a number of bottlenecks that include the rising number of published resear...
Knowledge bases built in the knowledge processing field have a problem in that experts have to add r...
Analysing the relationship between academia and industry allows us to understand how the knowledge p...
Fully structured semantic resources representing facts in the form of triples (i.e., knowledge graph...
In recent years we have seen the emergence of a variety of scholarly datasets. Typically these captu...
Several scholarly knowledge graphs have been proposed to model and analyze the academic landscape. H...
Scientific knowledge has been traditionally disseminated and preserved through research articles pub...
Things such as organizations, persons, or locations are ubiquitous in all texts circulating on the i...
A scientific paper can be divided into two major constructs which are Metadata and Full-body text. M...
We describe an annotation initiative to capture the scholarly contributions in natural language proc...
Natural language text, from messages on social media to articles in newspapers, constitutes a signif...
Knowledge graphs (KG) are large network of entities and relationships, tipically expressed as RDF tr...
In this paper, we present a preliminary approach that uses a set of NLP and Deep Learning methods fo...
Knowledge graphs (KG) are large networks of entities and relationships, typically expressed as RDF t...
The continuous growth of scientific literature brings innovations and, at the same time, raises new ...
Science communication has a number of bottlenecks that include the rising number of published resear...
Knowledge bases built in the knowledge processing field have a problem in that experts have to add r...
Analysing the relationship between academia and industry allows us to understand how the knowledge p...
Fully structured semantic resources representing facts in the form of triples (i.e., knowledge graph...
In recent years we have seen the emergence of a variety of scholarly datasets. Typically these captu...
Several scholarly knowledge graphs have been proposed to model and analyze the academic landscape. H...
Scientific knowledge has been traditionally disseminated and preserved through research articles pub...
Things such as organizations, persons, or locations are ubiquitous in all texts circulating on the i...
A scientific paper can be divided into two major constructs which are Metadata and Full-body text. M...
We describe an annotation initiative to capture the scholarly contributions in natural language proc...
Natural language text, from messages on social media to articles in newspapers, constitutes a signif...