@inproceedings{57324,
  abstract     = {{Generating SPARQL queries is crucial for extracting relevant information from diverse knowledge graphs. However, the structural and semantic differences among these graphs necessitate training or fine-tuning a tailored model for each one. In this paper, we propose UniQ-Gen, a unified query generation approach to generate SPARQL queries across various knowledge graphs. UniQ-Gen integrates entity recognition, disambiguation, and linking through a BERT-NER model and employs cross-encoder ranking to align questions with the Freebase ontology. We conducted several experiments on different benchmark datasets such as LC-QuAD 2.0, GrailQA, and QALD-10. The evaluation results demonstrate that our approach achieves performance equivalent to or better than models fine-tuned for individual knowledge graphs. This finding suggests that fine-tuning a unified model on a heterogeneous dataset of SPARQL queries across different knowledge graphs eliminates the need for separate models for each graph, thereby reducing resource requirements.}},
  author       = {{Vollmers, Daniel and Srivastava, Nikit and Zahera, Hamada Mohamed Abdelsamee and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{Knowledge Engineering and Knowledge Management}},
  editor       = {{Alam, Mehwish and Rospocher, Marco and van Erp, Marieke and Hollink, Laura and Gesese, Genet Asefa}},
  isbn         = {{978-3-031-77792-9}},
  pages        = {{174–189}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{UniQ-Gen: Unified Query Generation Across Multiple Knowledge Graphs}}},
  doi          = {{https://doi.org/10.1007/978-3-031-77792-9_11}},
  year         = {{2025}},
}