Construction of Hyper-Relational Knowledge Graphs Using Pre-Trained
  Large Language Models
Preetha Datta Fedor Vitiugin Anastasiia Chizhikova Nitin Sawhney
Extracting hyper-relations is crucial for constructing comprehensive
knowledge graphs, but there are limited supervised methods available for this
task. To address this gap, we introduce a zero-shot prompt-based method using
OpenAI's GPT-3.5 model for extracting hyper-relational knowledge from text.
Comparing our model with a baseline, we achieved promising results, with a
recall of 0.77. Although our precision is currently lower, a detailed analysis
of the model outputs has uncovered potential pathways for future research in
this area.