Event Ontology Expansion via LLM-Based Conceptualization

Jiafeng Guo; Long Bai; Weicheng Ren; Xiaolong Jin; Xueqi Cheng; Zixuan Li

arxiv: 2606.21048 · v1 · pith:J6OA3NNLnew · submitted 2026-06-19 · 💻 cs.CL

Event Ontology Expansion via LLM-Based Conceptualization

Weicheng Ren , Zixuan Li , Long Bai , Xiaolong Jin , Jiafeng Guo , Xueqi Cheng This is my paper

Pith reviewed 2026-06-26 14:52 UTC · model grok-4.3

classification 💻 cs.CL

keywords event ontology expansionLLM conceptualizationevent clusteringhierarchy expansionconcept-level semanticstrigger representationsACE ERE MAVEN

0 comments

The pith

LLM-generated concept names and descriptions improve event ontology expansion by enabling concept-level reasoning over contextualized triggers.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Event ontology expansion requires adding new event types to an existing hierarchy, but prior methods cluster events using sentence-specific trigger representations that mix stable type meaning with variable wording. The paper claims this mixing produces unstable clusters and unreliable hierarchy attachments. ConceptE instead prompts an LLM to output a short concept name and description for each trigger, then builds joint representations that align better with ontology-level decisions. On ACE, ERE, and MAVEN the method raises BCubed-F1 by up to 12.37 percent for clustering and Taxo_F1 by up to 6.48 percent for hierarchy placement. A reader would care because ontologies need repeatable type definitions rather than surface-form clusters that shift with each new sentence.

Core claim

ConceptE first derives concept-level semantics by prompting an LLM with the sentence and event trigger to produce a concise concept name and natural-language description. It then jointly encodes these semantics with trigger information to build concept-enhanced representations aligned with ontology-level reasoning. This design supports more coherent event clustering, more reliable hierarchy expansion, and ontology-consistent type naming.

What carries the argument

LLM-based conceptualization step that produces a concept name and natural-language description to replace or augment contextualized trigger representations for clustering and hierarchy tasks.

If this is right

Event clustering measured by BCubed-F1 becomes more coherent because concept names abstract away sentence-specific wording.
Hierarchy expansion measured by Taxo_F1 becomes more reliable because the representations match the level at which ontology edges are defined.
Induced event types receive names that remain consistent with the existing ontology vocabulary.
The same joint encoding of concept and trigger information applies across the subtasks of clustering, hierarchy attachment, and naming.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same LLM conceptualization step could be tested on entity or relation ontology expansion where instance representations also mix context with type semantics.
If the gain comes from moving to concept-level text, similar prompting might help any task that must aggregate many surface mentions into a single abstract category.
A direct follow-up would measure how often the LLM concept name matches an existing ontology node versus inventing a new label, to see whether naming consistency is the main driver of the Taxo_F1 lift.

Load-bearing premise

The LLM outputs for concept names and descriptions reliably reflect stable, ontology-aligned semantics rather than introducing their own noise or misalignment.

What would settle it

A controlled run on ACE, ERE, or MAVEN where representations built only from raw trigger embeddings produce higher BCubed-F1 or Taxo_F1 scores than the LLM-augmented versions.

Figures

Figures reproduced from arXiv: 2606.21048 by Jiafeng Guo, Long Bai, Weicheng Ren, Xiaolong Jin, Xueqi Cheng, Zixuan Li.

**Figure 2.** Figure 2: Overall framework of ConceptE. them to appropriate positions in Tknown, and generating ontology-consistent type names. Thus, the task jointly involves event clustering, hierarchy expansion, and type naming. 3 Method We propose ConceptE, a conceptualizationenhanced framework that derives concept-level semantics for event ontology expansion. ConceptE first derives concept-level semantics for event trigge… view at source ↗

**Figure 3.** Figure 3: Illustration of the conceptualization prompt. [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗

**Figure 4.** Figure 4: An example prompt for ontology-aware event [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗

**Figure 5.** Figure 5: Visualization of event clustering features under different t-SNE perplexity values. [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗

read the original 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 12.37\% in BCubed-F1 for event clustering and 6.48\% in Taxo\_F1 for hierarchy expansion, demonstrating the effectiveness of the proposed ConceptE method.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

ConceptE uses LLM prompts to add concept names and descriptions to trigger reps for event ontology expansion and reports clear gains on ACE/ERE/MAVEN, but the gains rest on an untested assumption that the LLM output supplies the right semantics.

read the letter

The paper's core move is to prompt an LLM for a short concept name and natural-language description from each trigger sentence, then jointly encode that with the original trigger to get representations better suited to clustering and hierarchy placement. This is a direct response to the known problem that contextualized triggers mix type semantics with local context noise.

It does produce measurable lifts: up to 12.37 points BCubed-F1 on clustering and 6.48 points Taxo_F1 on hierarchy expansion across the three datasets, beating the cited prior methods. The joint-encoding step and the explicit concept layer are the concrete technical additions.

The main soft spot is the missing isolation. The framework description says the LLM output is combined with trigger information, yet there is no ablation that swaps in random text, template paraphrases, or human-written concepts while holding the rest fixed. Without that, the numeric gains could come from simply having more text in the encoder rather than from ontology-aligned semantics. The abstract also gives no human ratings or error analysis on whether the generated concepts actually sit at the right level of generality.

This is incremental work aimed at people already running event ontology expansion pipelines. It has enough concrete results on standard data to justify sending it out for review, though any referee will want the ablations and a clearer check on concept quality before accepting the central claim.

Referee Report

2 major / 1 minor

Summary. The paper proposes ConceptE, an LLM-based framework for event ontology expansion. It generates concept names and natural-language descriptions from event triggers and sentences via prompting, jointly encodes them with trigger representations to produce concept-enhanced embeddings, and uses these for event clustering and hierarchy expansion. Experiments on ACE, ERE, and MAVEN report consistent outperformance over prior SOTA methods, with gains of up to 12.37% BCubed-F1 on clustering and 6.48% Taxo_F1 on hierarchy expansion.

Significance. If the central claim holds, the work offers a concrete mechanism for injecting concept-level semantics into event ontology tasks, addressing the instability of purely contextualized trigger representations. The multi-dataset evaluation and focus on both clustering and taxonomic attachment are positive features of the empirical design.

major comments (2)

[Experiments (ACE/ERE/MAVEN results)] The abstract and method description state that LLM-generated concept names and descriptions supply ontology-aligned semantics that drive the reported gains, yet the experimental evaluation provides no ablation that holds the joint-encoding pipeline fixed while replacing the LLM outputs with non-semantic controls (random strings, template paraphrases, or trigger-only input). Without this isolation, the numeric improvements cannot be attributed specifically to conceptualization rather than richer input representations in general.
[Method and Experiments] The paper claims the joint encoding produces representations 'aligned with ontology-level reasoning,' but reports no human evaluation or inter-annotator agreement on whether the LLM-generated concept names/descriptions are in fact more stable or ontology-consistent than the original triggers on the same instances used for the BCubed-F1 and Taxo_F1 measurements.

minor comments (1)

[Abstract] The abstract does not name the specific LLM, prompting template, or temperature settings used for concept generation; these details are needed for reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and indicate the revisions planned for the next version of the manuscript.

read point-by-point responses

Referee: The abstract and method description state that LLM-generated concept names and descriptions supply ontology-aligned semantics that drive the reported gains, yet the experimental evaluation provides no ablation that holds the joint-encoding pipeline fixed while replacing the LLM outputs with non-semantic controls (random strings, template paraphrases, or trigger-only input). Without this isolation, the numeric improvements cannot be attributed specifically to conceptualization rather than richer input representations in general.

Authors: We agree that an explicit ablation isolating the semantic contribution of the LLM outputs would strengthen attribution of the gains. In the revised manuscript we will add experiments that keep the joint-encoding pipeline fixed while replacing the LLM-generated concept names and descriptions with random strings, template paraphrases, and trigger-only inputs. These controls will help demonstrate that the observed improvements stem specifically from the conceptualization step rather than from richer input representations alone. revision: yes
Referee: The paper claims the joint encoding produces representations 'aligned with ontology-level reasoning,' but reports no human evaluation or inter-annotator agreement on whether the LLM-generated concept names/descriptions are in fact more stable or ontology-consistent than the original triggers on the same instances used for the BCubed-F1 and Taxo_F1 measurements.

Authors: We acknowledge that direct human evaluation with inter-annotator agreement would provide valuable corroboration of the ontology alignment and stability claims. Our current evidence rests on consistent quantitative gains across three datasets and two subtasks. In the revision we will add a qualitative analysis section with representative examples comparing LLM-generated concepts to original triggers, and we will explore the feasibility of a small-scale human study on a subset of instances; if resource constraints prevent a full IAA study, the qualitative examples will still be included. revision: partial

Circularity Check

0 steps flagged

No derivation chain present; empirical evaluation is self-contained.

full rationale

The paper presents a framework (ConceptE) that uses LLM prompting to generate concept names and descriptions, then jointly encodes them for clustering and hierarchy expansion. All claims rest on reported experimental metrics (BCubed-F1, Taxo_F1) across fixed datasets (ACE, ERE, MAVEN). No equations, fitted parameters renamed as predictions, or self-citation chains appear in the provided text. The performance numbers are external measurements, not quantities defined by the method itself, so no reduction to inputs by construction occurs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Based on abstract only; the central method rests on the untested premise that LLM outputs provide superior concept-level semantics.

axioms (1)

domain assumption Prompting an LLM with sentence and event trigger produces a concise concept name and natural-language description that capture concept-level semantics better than contextualized embeddings.
This is the core mechanism introduced to solve the stated problem of conflating semantics with surface context.

pith-pipeline@v0.9.1-grok · 5759 in / 1280 out tokens · 15164 ms · 2026-06-26T14:52:34.749876+00:00 · methodology

discussion (0)

Reference graph

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CEO: Corpus-based open-domain event ontology in- duction. InFindings of the Association for Compu- tational Linguistics: EACL 2024, pages 946–964, St. Julian’s, Malta. Association for Computational Linguistics. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, and 1 others

2024

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Presents the Qwen3 series, including dense and MoE mod- els, hybrid thinking mode, and the thinking budget mechanism

Qwen3 technical report.arXiv preprint arXiv:2505.09388. Presents the Qwen3 series, including dense and MoE mod- els, hybrid thinking mode, and the thinking budget mechanism. Yue Yu, Yinghao Li, Jiaming Shen, Hao Feng, Jimeng Sun, and Chao Zhang

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Code- Taxo: Enhancing taxonomy expansion with limited examples via code language prompts. InFindings of the Association for Computational Linguistics: ACL 2025, pages 4131–4144, Vienna, Austria. Associa- tion for Computational Linguistics. Tianyi Zhang*, Varsha Kishore*, Felix Wu*, Kilian Q. Weinberger, and Yoav Artzi

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Bertscore: Eval- uating text generation with bert. InInternational Conference on Learning Representations. A Dataset Statistics Table 6 reports the split of known and unknown event types used in our experiments. Following HALTON (Cao et al., 2023), we simulate the real- istic ontology expansion scenario where only fre- quent event types are covered by the...

2023

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We train the clustering module with Adam using a learning rate of 1×10 −4, batch size 64, and 60 epochs

Trigger spans and concept spans are pooled with max pool- ing. We train the clustering module with Adam using a learning rate of 1×10 −4, batch size 64, and 60 epochs. The supervised contrastive temper- ature is 0.07, and the center loss weight is 0.005. For unlabeled neighborhood mining, K= 2 for ACE and ERE and K= 15 for MA VEN. Thek- means pseudo label...

2025