DBpedia NIF: Open, Large-Scale and Multilingual Knowledge Extraction Corpus

By Milan Dojchinovski, Julio Hernandez, M. Ackermann, Amit Kirschenbaum, Sebastian Hellmann. 2018

Subfields: Information Extraction, Knowledge Graphs & Representation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Hayden S. Helm
2. Carey E. Priebe
3. Brandon Duderstadt
4. Aranyak Acharyya

Citations in the corpus

• Comparing Foundation Models using Data Kernels (2023, methodology): “To test the affect of a training data intervention on the data kernel, we trained two randomly initialized BERT models – a “baseline" model and a “plant-ablated” model – on two different versions of the DBPedia14 corpus [8].”