TY - JOUR
AU - Martin, Michael
AB - AbstractWhen we look at the global knowledge graph landscape, we quickly find that there are billions of interconnected facts that have the potential to answer all kinds of questions. However, a persistent challenge lies in finding corresponding questions that align with these facts. The availability of these questions along with matching SPARQL queries is an important prerequisite for fine-tuning Large Language Models for domain-specific query generation, which is why we propose Queryfy, a novel framework that leverages Large Language Models to automate the task of deriving questions and queries from knowledge graphs, empowering users to harness their full potential.
TI - Queryfy: from knowledge graphs to questions using open Large Language Models
JF - it - Information Technology
DO - 10.1515/itit-2024-0079
DA - 2025-02-01
UR - https://www.deepdyve.com/lp/de-gruyter/queryfy-from-knowledge-graphs-to-questions-using-open-large-language-4zDiBCKQbh
SP - 54
EP - 61
VL - 67
IS - 1
DP - DeepDyve
ER -