@inproceedings{kamaladdini-ezzabady-benamara-2025-entity,
    title = "Entity Quality Enhancement in Knowledge Graphs through {LLM}-based Question Answering",
    author = "Kamaladdini Ezzabady, Morteza  and
      Benamara, Farah",
    editor = "Gesese, Genet Asefa  and
      Sack, Harald  and
      Paulheim, Heiko  and
      Merono-Penuela, Albert  and
      Chen, Lihu",
    booktitle = "Proceedings of the Workshop on Generative AI and Knowledge Graphs (GenAIK)",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "International Committee on Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.genaik-1.14/",
    pages = "136--145",
    abstract = "Most models for triple extraction from texts primarily focus on named entities. However, real-world applications often comprise non-named entities that pose serious challenges for entity linking and disambiguation. We focus on these entities and propose the first LLM-based entity revision framework to improve the quality of extracted triples via a multi-choice question-answering mechanism. When evaluated on two benchmark datasets, our results show a significant improvement, thereby generating more reliable triples for knowledge graphs."
}