We create a Knowledge Graph for Humanities research. Starting with a multidisciplinary dataset of 25,000 OCRed JSTOR papers, we use Deep Learning methods to filter out OCR noise, extract and interrelate research activities, methods and goals, associate them with metadata and transform each paper into approximately 200 RDF triples
Nowadays a lot of data is in the form of Knowledge Graphs, i.e. a set of nodes and relationships bet...
International audienceWith the generalisation of the digitalization of scientific publications , sch...
The Rich Context project at NYU Wagner is the knowledge graph complement to the ADRF platform for cr...
Knowledge graphs (KG) are large networks of entities and relationships, typically expressed as RDF t...
In this paper, we present a preliminary approach that uses a set of NLP and Deep Learning methods fo...
The amount of scientific literature continuously grows, which poses an increasing challenge for rese...
The continuous growth of scientific literature brings innovations and, at the same time, raises new ...
The ever-increasing number of published scholarly articles imposes significant challenges in organiz...
Nowadays, scientific articles are mostly published as PDF files containing unstructured and semi-str...
Scientific knowledge has been traditionally disseminated and preserved through research articles pub...
Knowledge graphs (KG) are large network of entities and relationships, tipically expressed as RDF tr...
Science communication has a number of bottlenecks that include the rising number of published resear...
Knowledge graphs represent the meaning of properties of real-world entities and relationships among ...
Fully structured semantic resources representing facts in the form of triples (i.e., knowledge graph...
Knowledge bases built in the knowledge processing field have a problem in that experts have to add r...
Nowadays a lot of data is in the form of Knowledge Graphs, i.e. a set of nodes and relationships bet...
International audienceWith the generalisation of the digitalization of scientific publications , sch...
The Rich Context project at NYU Wagner is the knowledge graph complement to the ADRF platform for cr...
Knowledge graphs (KG) are large networks of entities and relationships, typically expressed as RDF t...
In this paper, we present a preliminary approach that uses a set of NLP and Deep Learning methods fo...
The amount of scientific literature continuously grows, which poses an increasing challenge for rese...
The continuous growth of scientific literature brings innovations and, at the same time, raises new ...
The ever-increasing number of published scholarly articles imposes significant challenges in organiz...
Nowadays, scientific articles are mostly published as PDF files containing unstructured and semi-str...
Scientific knowledge has been traditionally disseminated and preserved through research articles pub...
Knowledge graphs (KG) are large network of entities and relationships, tipically expressed as RDF tr...
Science communication has a number of bottlenecks that include the rising number of published resear...
Knowledge graphs represent the meaning of properties of real-world entities and relationships among ...
Fully structured semantic resources representing facts in the form of triples (i.e., knowledge graph...
Knowledge bases built in the knowledge processing field have a problem in that experts have to add r...
Nowadays a lot of data is in the form of Knowledge Graphs, i.e. a set of nodes and relationships bet...
International audienceWith the generalisation of the digitalization of scientific publications , sch...
The Rich Context project at NYU Wagner is the knowledge graph complement to the ADRF platform for cr...
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