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REVIEW 4 major objections 4 minor 47 references

Enhancing Rhetorical Figure Annotation: An Ontology-Based Web Application with RAG Integration

T0 review · 4 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read The paper claims that combining a reified rhetorical-figure ontology with retrieval-augmented generation lets untrained users annotate German rhetorical figures, with basic chunking (size 2048, top-12 reranked to top-6) as the best RAG…

desk verdict A genuine engineering contribution—reified GRhOOT ontology, a German annotation web app, and a RAG pipeline—whose evaluation is too weak to support the claims as written. read the letter →

arxiv 2412.13799 v1 pith:Z625TFAD submitted 2024-12-18 cs.CL cs.AI

Ramona Kühn , Jelena Mitrović , Michael Granitzer This is my paper · ORCID
classification cs.CLcs.AI
keywords rhetoricalfiguresontologyreificationGRhOOTretrieval-augmentedgenerationannotationtoolGermanlanguagedatascarcityRAGevaluation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper builds a web application, “Find your Figure,” that lets people without linguistic training identify and annotate rhetorical figures in German by selecting descriptive properties of a text instead of knowing figure names. The German rhetorical ontology GRhOOT is restructured, or reified, so that compound relations are split into three small, queryable property dimensions: operation, affected element, and operational form. To make interaction more natural, the app adds a chat feature powered by retrieval-augmented generation, in which the reified ontology is chunked, embedded, and retrieved to ground a large language model's answers. The paper identifies the best RAG configuration for this application as basic chunking with a chunk size of 2048 and top-12 retrieval reranked to top-6. The authors state this is one of the first practical uses of a rhetorical ontology for figure annotation and that it shows promising results, toward easing the scarcity of annotated German rhetorical figures.

What carries the argument

The reified GRhOOT ontology is the central object. In the original ontology, a figure like epiphora was described by compound relations such as 'isRepeatableElementOfSameForm'; reification splits these into three fine-grained properties, hasOperation, affectedElement, and operationalForm, on which users can filter. This decomposition is what lets a non-expert describe a figure's pattern instead of knowing its name. The second load-bearing mechanism is the RAG pipeline: the ontology is chunked and embedded into a vector store, a user question is embedded and matched against chunks, the top chunks are reranked, and a language model answers with that retrieved context. The paper evaluates the pipeline using ontology competency questions and an automated RAG evaluation suite, comparing chunk sizes, chunking methods, and retrieval depths to select the best setting.

What would settle it

Give the application to a group of untrained, non-expert German speakers with a set of sentences that contain figures based on transferred meaning, such as metaphors, and measure how often their property selections lead to the correct figure name. A success rate near chance for those figures would show that the property-based flow does not support untrained annotation for the very class of figures the paper admits are hardest.

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Extended reading notes

Core claim

The paper's central claim is that an ontology of rhetorical figures can be made practically useful for annotation by reifying its relations into user-facing property dimensions and by grounding a language-model chat in retrieval from that ontology. A user who spots a pattern, such as repetition of a word at the same position, selects the matching properties from dropdowns; the app translates the selection into a SPARQL query and returns candidate figures with definitions and examples. The same reified ontology serves as the retrieval source for a RAG-powered chat, and the paper reports that a basic chunking setting (chunk size 2048, retrieve top-12 then rerank to top-6) yields the best answer-oriented metrics among the configurations tested. The approach is described as one of the first to combine a rhetorical ontology with RAG for practical annotation, and the paper claims it shows promising results, while noting that figures based on transferred meaning, such as metaphor, remain harder for this property-selection flow.

Load-bearing premise

The entire annotation flow assumes that a user without linguistic training can reliably map a text to the three property dimensions of operation, affected element, and operational form; if users cannot do that, the app returns the wrong candidates and the collected data lose quality.

Editorial extensions

If this is right

  • Users without linguistic training can submit German examples and receive candidate figure names with definitions, which lowers the barrier to creating annotated data.
  • Because figures can be queried by operation, affected element, or operational form, the same ontology supports flexible search paths rather than requiring users to know figure names in advance.
  • The RAG configuration identified in the paper, basic chunking with a chunk size of 2048 and top-12 to top-6 reranking, can serve as a default starting point for other ontology-based RAG applications.
  • Questions that require aggregating information across multiple ontology chunks (for example, listing all figures with a given property) remain a weak spot for the RAG chat, so the app is better suited to direct look-ups.
  • Once user-submitted examples are verified, they can be added back to the ontology and the vector store, giving a concrete route to grow the sparse German rhetorical-figure data.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same reification pattern could transfer to other low-resource annotation domains: any domain ontology whose compound relations can be split into user-facing property dimensions could power a similar property-selection interface.
  • The RAG result was tuned on a small, compact ontology; larger or more heterogeneous knowledge bases might not favor basic chunking at 2048, so the optimal setting is likely scale-dependent.
  • Because the LLM's wordy answers and modern paraphrasing lowered semantic-similarity scores, future evaluations should separate factual correctness from phrasing, for example by also scoring exact figure-name matches.
  • A head-to-head comparison of the dropdown property flow against the RAG chat flow, using the same sentences and annotators, would show which interface yields more accurate annotations and where the 'No idea' fallback is needed.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper presents "Find your Figure," a Flask-based web application for annotating rhetorical figures in German, built on a reified version of the GRhOOT ontology. Users select properties such as operation, affected element, and operational form from dropdowns; the backend translates these into a SPARQL query and returns candidate figures. A separate chat page integrates a retrieval-augmented generation (RAG) pipeline using gpt-3.5-turbo, bge-m3 embeddings, and a reranker. The authors evaluate several chunk sizes and retrieval settings with the Ragas framework on 70 competency questions derived from the ontology, and they conclude that basic chunking with chunk size 2048 and top-12 retrieval reranked to top-6 is the optimal configuration. They also describe input verification mechanisms, limitations for figures based on transferred meaning, and ethical considerations around intellectual property.

Significance. The paper addresses a real bottleneck in computational rhetoric: the scarcity of annotated data for non-English and non-metaphor rhetorical figures. The ontology restructuring, the openly available code, and the integration of RAG with a rhetorical ontology are useful contributions if the system works as described. The significance is currently limited, however, because the annotation workflow is not validated with users, and the RAG evaluation does not compare against a no-RAG baseline or provide error bars. The claim of identifying the optimal RAG configuration is therefore not yet supported, and the central data-collection goal rests on an untested assumption about untrained annotators.

major comments (4)
  1. [§5.1] The RAG evaluation is circular with respect to the ontology. The 70 competency questions are either template questions asking for properties of figures or questions generated by gpt-4 from formalizations of the same ontology; the ground-truth answers are obtained by running SPARQL queries on that ontology, and the context is manually extracted from that same ontology. Since the retrieval source and the answer source are identical, high context recall and high faithfulness partly measure whether the retriever finds the exact passage from which the answer was derived, not whether the pipeline produces correct answers on an independent task. Please either reframe the evaluation as a retrieval-coverage check on a single knowledge source or construct ground truth from an external corpus (e.g., dictionary definitions or expert-verified examples).
  2. [§5.2, Table 2] The choice of basic chunking 2048 with top-12/6 as the optimal setting is not consistent with the stated priority on answer metrics. In Table 2, this row has answer correctness 0.7355 and answer similarity 0.8655, while the AMR 512/256/128 top-6/3 row has answer correctness 0.8619 and the basic 2048 top-6/3 row has answer similarity 0.9673. If answer correctness and answer similarity are the most important metrics, as claimed at the end of §5.1, the selected configuration is not the best on those metrics. Please specify the exact selection criterion (e.g., a weighted combination or a threshold) and justify why the chosen setting is preferred over rows that score higher on answer correctness or answer similarity.
  3. [§4.1, §7] The core annotation workflow rests on an untested assumption: that users without linguistic knowledge can map a text onto the reified property dimensions (operation, affected element, operational form) and that the resulting SPARQL query reliably surfaces the intended rhetorical figure. The paper provides no user study, no inter-annotator agreement, and no pilot test of the FyF.html dropdown workflow. Section 7 concedes that the application is better suited to figures with obvious lexical patterns than to figures relying on transferred meaning, such as metaphors. That is not merely a user-side limitation; it directly limits the kind of data the application can collect. Please report at least a small pilot evaluation with untrained users, including agreement or accuracy against expert labels, or explicitly scope the application's data-collection claim to lexical-pattern figures.
  4. [§5] The evaluation compares only RAG configurations and never includes a no-RAG baseline, such as prompting the same LLM without retrieved ontology context or a retrieval-only baseline. Without such a baseline, the claim that RAG improves answer quality over a plain LLM is not supported. In addition, Table 2 reports a single run per configuration without standard deviations or repeated trials; even at temperature 0.1, LLM output is stochastic and the post-processing step (described in §5.1) could introduce run-to-run variation. Please add a no-RAG baseline and repeated runs with variance reporting, or explicitly weaken the conclusion to a comparison among the tested RAG settings rather than an identification of the globally optimal configuration.
minor comments (4)
  1. [§3, Table 1] The property name is inconsistent: Section 3 writes `:operationalForm :SameForm`, while Table 1 uses `hasOperationForm`. Please standardize the property names in the text, the table, and the ontology itself.
  2. [§5.1] The sentence "The answers of the LLM still require post-processing before we can use them in the Ragas framework" is clear, but the paper does not explain whether the same post-processing is applied consistently to all configurations or whether it could interact with the chunking settings. Please clarify in the evaluation description.
  3. [§4.2] The text says "we will flag the example in the database in the columnis_invalid," which appears to be a missing-space typo for `is_invalid`. Please fix the formatting.
  4. [§5.2] The phrase "We notice deviations in answer correctness and answer similarity ," contains an extra space before the comma. Please correct the typo.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the ontology-internal RAG evaluation matches the paper's stated objective of retrieving ontology-specific definitions.

full rationale

The paper's only potentially self-referential step is the RAG evaluation in Section 5.1: the Ragas ground-truth file is built from competency questions whose answers are extracted from the reified GRhOOT ontology, and the retrieval corpus is the same reified GRhOOT ontology. This makes the evaluation internal, in that context recall and faithfulness measure recovery of ontology content rather than external linguistic correctness. However, this is not circularity in the prohibited sense: the paper explicitly states that the LLM should respond with the ontology's own definitions to ensure consistent annotation guidelines, so ontology-derived ground truth is the appropriate target for the stated objective. The competency questions are not defined in terms of RAG outputs, no fitted parameter is relabeled as a prediction, and the choice among chunking and reranking settings is an honest model-selection comparison rather than a derivation. The reification in Section 3 is a presentational restructuring of the authors' prior GRhOOT ontology, not a derived result claiming independent empirical content. The Section 7 limitation about metaphors and the absence of a user study are validity/scope concerns, not circular steps. Self-citations to prior ontology work are antecedent contributions, not load-bearing uniqueness claims. No enumerated circularity pattern is present.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claims rest on the correctness and usability of the reified GRhOOT ontology, on the validity of gpt-4 generated competency questions as an evaluation set, and on the faithfulness of Ragas LLM-based metrics. The selected RAG configuration is a hand-tuned choice from a small grid, and the ground truth for evaluation is internal to the ontology. The ontology itself and the evaluation artifacts are the main borrowed inputs, plus the external LLM, embedding, and reranker models.

free parameters (4)
  • chunk_size = 2048 (basic chunking)
    Selected as the best setting from Table 2 after testing [2048], [2048,512,128], and [512,256,128]. The chosen configuration directly determines RAG answer quality.
  • retrieval_top_k and rerank_k = 12 then 6 (setting alpha)
    The top-12 chunks retrieved and reranked to top-6 produced the best answer-focused scores; the paper labels this the optimal setting.
  • LLM temperature = 0.1
    Set manually to obtain stable responses; no experiment varies this value.
  • embedding and reranker models = bge-m3 / bge-reranker-large
    Fixed model choices treated as constants; no comparison with other embedding models or rerankers.
assumptions (4)
  • domain assumption The GRhOOT ontology's definitions and relations for 110 German rhetorical figures are accurate enough to guide annotation.
    The web app and RAG context both draw knowledge exclusively from this ontology; any error in the ontology propagates to the annotations (Sections 3 and 4).
  • ad hoc to paper The reified properties hasOperation, affectedElement, and hasOperationForm are sufficient and intuitive for users without linguistic training to describe rhetorical figures.
    The entire guided annotation flow depends on a user's ability to map a text to these three property dimensions; the authors note in Section 7 that non-lexical figures such as metaphor are hard for users, so this assumption is fragile.
  • domain assumption Competency questions generated by gpt-4 from a single figure formalization are a valid evaluation set.
    The 70 CQs include gpt-4-generated questions with no triple extraction step; their validity is asserted rather than independently checked (Section 5.1).
  • domain assumption Ragas LLM-based metrics faithfully measure answer quality.
    The paper's own analysis in Section 5.2 shows the metrics penalize wordy but correct answers, indicating that the metrics are noisy proxies for the user experience.

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Cite this review

Pith. "Pith review of Enhancing Rhetorical Figure Annotation: An Ontology-Based Web Application with RAG Integration." pith.science (2026). https://pith.science/paper/Z625TFAD

@misc{pith2026241213799,
  author       = {Pith},
  title        = {Pith review of: Enhancing Rhetorical Figure Annotation: An Ontology-Based Web Application with RAG Integration},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Z625TFAD}},
  note         = {Machine review of arXiv:2412.13799}
}
read the original abstract

Rhetorical figures play an important role in our communication. They are used to convey subtle, implicit meaning, or to emphasize statements. We notice them in hate speech, fake news, and propaganda. By improving the systems for computational detection of rhetorical figures, we can also improve tasks such as hate speech and fake news detection, sentiment analysis, opinion mining, or argument mining. Unfortunately, there is a lack of annotated data, as well as qualified annotators that would help us build large corpora to train machine learning models for the detection of rhetorical figures. The situation is particularly difficult in languages other than English, and for rhetorical figures other than metaphor, sarcasm, and irony. To overcome this issue, we develop a web application called "Find your Figure" that facilitates the identification and annotation of German rhetorical figures. The application is based on the German Rhetorical ontology GRhOOT which we have specially adapted for this purpose. In addition, we improve the user experience with Retrieval Augmented Generation (RAG). In this paper, we present the restructuring of the ontology, the development of the web application, and the built-in RAG pipeline. We also identify the optimal RAG settings for our application. Our approach is one of the first to practically use rhetorical ontologies in combination with RAG and shows promising results.

Figures

Figures reproduced from arXiv: 2412.13799 by the authors.

Figure 1
Figure 1. Powerful LLMs such as ChatGPT still fail [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Scheme of the SQL Lite Database. The arrows [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. The page FyF.html helps users to find the name of a rhetorical figure hidden in a text. Properties can be selected from the dropdown lists [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: The result of the user’s submission. Rhetorical [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Overview of the integrated RAG Pipeline for the GRhOOT ontology. [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: The formal model of an epiphora in the GRhOOT ontology, illustrating the relations between the [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]
Figure 7
Figure 7. Figure 7: The formal model of an epiphora in the reified GRhOOT ontology. Compared to the model in the original [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]

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