@inproceedings{ren-etal-2026-event,
title = "Event Ontology Expansion via {LLM}-Based Conceptualization",
author = "Ren, Weicheng and
Li, Zixuan and
Bai, Long and
Jin, Xiaolong and
Guo, Jiafeng and
Cheng, Xueqi",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.737/",
pages = "15473--15489",
ISBN = "979-8-89176-462-0",
abstract = "Event ontology expansion aims to discover emerging event types from data and extend them to appropriate positions in the existing event ontology. Existing methods typically cluster contextualized trigger representations and attach induced clusters to the ontology based on instance-level similarity. However, ontology expansion requires concept-level semantics that characterize event types, whereas contextualized trigger representations often conflate these semantics with surface contextual variation, leading to unstable clustering and unreliable hierarchy expansion. To address this issue, we propose ConceptE, a conceptualization-enhanced framework for event ontology expansion. ConceptE first derives concept-level semantics by prompting an LLM with the sentence and event trigger, producing a concise concept name and a natural-language description. It then jointly encodes these semantics with trigger information to build concept-enhanced representations aligned with ontology-level reasoning. This representation design supports more coherent event clustering, more reliable hierarchy expansion, and ontology-consistent type naming. Experiments on ACE, ERE, and MAVEN demonstrate that ConceptE consistently outperforms state-of-the-art approaches across all subtasks of event ontology expansion. In particular, it achieves improvements of up to 10.85{\%} in BCubed-F1 for event clustering and 6.48{\%} in Taxo{\_}F1 for hierarchy expansion, demonstrating the effectiveness of the proposed ConceptE method. The code is available at https://github.com/ICT-KCG/ConceptE."
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<abstract>Event ontology expansion aims to discover emerging event types from data and extend them to appropriate positions in the existing event ontology. Existing methods typically cluster contextualized trigger representations and attach induced clusters to the ontology based on instance-level similarity. However, ontology expansion requires concept-level semantics that characterize event types, whereas contextualized trigger representations often conflate these semantics with surface contextual variation, leading to unstable clustering and unreliable hierarchy expansion. To address this issue, we propose ConceptE, a conceptualization-enhanced framework for event ontology expansion. ConceptE first derives concept-level semantics by prompting an LLM with the sentence and event trigger, producing a concise concept name and a natural-language description. It then jointly encodes these semantics with trigger information to build concept-enhanced representations aligned with ontology-level reasoning. This representation design supports more coherent event clustering, more reliable hierarchy expansion, and ontology-consistent type naming. Experiments on ACE, ERE, and MAVEN demonstrate that ConceptE consistently outperforms state-of-the-art approaches across all subtasks of event ontology expansion. In particular, it achieves improvements of up to 10.85% in BCubed-F1 for event clustering and 6.48% in Taxo_F1 for hierarchy expansion, demonstrating the effectiveness of the proposed ConceptE method. The code is available at https://github.com/ICT-KCG/ConceptE.</abstract>
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%0 Conference Proceedings
%T Event Ontology Expansion via LLM-Based Conceptualization
%A Ren, Weicheng
%A Li, Zixuan
%A Bai, Long
%A Jin, Xiaolong
%A Guo, Jiafeng
%A Cheng, Xueqi
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F ren-etal-2026-event
%X Event ontology expansion aims to discover emerging event types from data and extend them to appropriate positions in the existing event ontology. Existing methods typically cluster contextualized trigger representations and attach induced clusters to the ontology based on instance-level similarity. However, ontology expansion requires concept-level semantics that characterize event types, whereas contextualized trigger representations often conflate these semantics with surface contextual variation, leading to unstable clustering and unreliable hierarchy expansion. To address this issue, we propose ConceptE, a conceptualization-enhanced framework for event ontology expansion. ConceptE first derives concept-level semantics by prompting an LLM with the sentence and event trigger, producing a concise concept name and a natural-language description. It then jointly encodes these semantics with trigger information to build concept-enhanced representations aligned with ontology-level reasoning. This representation design supports more coherent event clustering, more reliable hierarchy expansion, and ontology-consistent type naming. Experiments on ACE, ERE, and MAVEN demonstrate that ConceptE consistently outperforms state-of-the-art approaches across all subtasks of event ontology expansion. In particular, it achieves improvements of up to 10.85% in BCubed-F1 for event clustering and 6.48% in Taxo_F1 for hierarchy expansion, demonstrating the effectiveness of the proposed ConceptE method. The code is available at https://github.com/ICT-KCG/ConceptE.
%U https://aclanthology.org/2026.findings-emnlp.737/
%P 15473-15489
Markdown (Informal)
[Event Ontology Expansion via LLM-Based Conceptualization](https://aclanthology.org/2026.findings-emnlp.737/) (Ren et al., Findings 2026)
ACL
- Weicheng Ren, Zixuan Li, Long Bai, Xiaolong Jin, Jiafeng Guo, and Xueqi Cheng. 2026. Event Ontology Expansion via LLM-Based Conceptualization. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 15473–15489, Budapest, Hungary. Association for Computational Linguistics.