@inproceedings{xu-etal-2026-zero,
title = "Zero-Shot Open-Schema Entity Structure Discovery",
author = "Xu, Xueqiang and
Xiao, Jinfeng and
Barry, James and
Elkaref, Mohab and
Zou, Jiaru and
Jiang, Pengcheng and
Zhang, Yunyi and
Giammona, Maxwell J and
Mel, Geeth De and
Han, Jiawei",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-long.354/",
pages = "7547--7561",
ISBN = "979-8-89176-380-7",
abstract = "Entity structure extraction, which aims to extract entities and their associated attribute{--}value structures from text, is an essential task for text understanding and knowledge graph construction. Existing methods based on large language models (LLMs) typically rely heavily on predefined entity attribute schemas or annotated datasets, often leading to incomplete extraction results. To address these challenges, we introduce ZOES, a novel approach to entity structure extraction that does not require any schema or annotated samples. ZOES operates via a principled mechanism of enrichment, refinement, and unification, based on the insight that an entity and its associated structure are mutually reinforcing. Experiments demonstrate that ZOES consistently enhances LLMs' ability to extract more complete entity structures across three different domains, showcasing both the effectiveness and generalizability of the method. These findings suggest that such an enrichment, refinement, and unification mechanism may serve as a principled approach to improving the quality of LLM-based entity structure discovery in various scenarios."
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<abstract>Entity structure extraction, which aims to extract entities and their associated attribute–value structures from text, is an essential task for text understanding and knowledge graph construction. Existing methods based on large language models (LLMs) typically rely heavily on predefined entity attribute schemas or annotated datasets, often leading to incomplete extraction results. To address these challenges, we introduce ZOES, a novel approach to entity structure extraction that does not require any schema or annotated samples. ZOES operates via a principled mechanism of enrichment, refinement, and unification, based on the insight that an entity and its associated structure are mutually reinforcing. Experiments demonstrate that ZOES consistently enhances LLMs’ ability to extract more complete entity structures across three different domains, showcasing both the effectiveness and generalizability of the method. These findings suggest that such an enrichment, refinement, and unification mechanism may serve as a principled approach to improving the quality of LLM-based entity structure discovery in various scenarios.</abstract>
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%0 Conference Proceedings
%T Zero-Shot Open-Schema Entity Structure Discovery
%A Xu, Xueqiang
%A Xiao, Jinfeng
%A Barry, James
%A Elkaref, Mohab
%A Zou, Jiaru
%A Jiang, Pengcheng
%A Zhang, Yunyi
%A Giammona, Maxwell J.
%A Mel, Geeth De
%A Han, Jiawei
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F xu-etal-2026-zero
%X Entity structure extraction, which aims to extract entities and their associated attribute–value structures from text, is an essential task for text understanding and knowledge graph construction. Existing methods based on large language models (LLMs) typically rely heavily on predefined entity attribute schemas or annotated datasets, often leading to incomplete extraction results. To address these challenges, we introduce ZOES, a novel approach to entity structure extraction that does not require any schema or annotated samples. ZOES operates via a principled mechanism of enrichment, refinement, and unification, based on the insight that an entity and its associated structure are mutually reinforcing. Experiments demonstrate that ZOES consistently enhances LLMs’ ability to extract more complete entity structures across three different domains, showcasing both the effectiveness and generalizability of the method. These findings suggest that such an enrichment, refinement, and unification mechanism may serve as a principled approach to improving the quality of LLM-based entity structure discovery in various scenarios.
%U https://aclanthology.org/2026.eacl-long.354/
%P 7547-7561
Markdown (Informal)
[Zero-Shot Open-Schema Entity Structure Discovery](https://aclanthology.org/2026.eacl-long.354/) (Xu et al., EACL 2026)
ACL
- Xueqiang Xu, Jinfeng Xiao, James Barry, Mohab Elkaref, Jiaru Zou, Pengcheng Jiang, Yunyi Zhang, Maxwell J Giammona, Geeth De Mel, and Jiawei Han. 2026. Zero-Shot Open-Schema Entity Structure Discovery. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7547–7561, Rabat, Morocco. Association for Computational Linguistics.