@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://aclanthology.org/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."
}
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%0 Conference Proceedings
%T Entity Quality Enhancement in Knowledge Graphs through LLM-based Question Answering
%A Kamaladdini Ezzabady, Morteza
%A Benamara, Farah
%Y Gesese, Genet Asefa
%Y Sack, Harald
%Y Paulheim, Heiko
%Y Merono-Penuela, Albert
%Y Chen, Lihu
%S Proceedings of the Workshop on Generative AI and Knowledge Graphs (GenAIK)
%D 2025
%8 January
%I International Committee on Computational Linguistics
%C Abu Dhabi, UAE
%F kamaladdini-ezzabady-benamara-2025-entity
%X 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.
%U https://aclanthology.org/2025.genaik-1.14/
%P 136-145
Markdown (Informal)
[Entity Quality Enhancement in Knowledge Graphs through LLM-based Question Answering](https://aclanthology.org/2025.genaik-1.14/) (Kamaladdini Ezzabady & Benamara, GenAIK 2025)
ACL