Homa at SemEval-2025 Task 5: Aligning Librarian Records with OntoAligner for Subject Tagging

Hadi Bayrami Asl Tekanlou; Hamed Babaei Giglou; Jafar Razmara; Mahsa Sanaei; Mostafa Rahgouy

REVIEW 3 major objections 6 minor 24 references

Homa at SemEval-2025 Task 5: Aligning Librarian Records with OntoAligner for Subject Tagging

T0 review · 3 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read A retrieval-augmented ontology-alignment pipeline can propose relevant GND subject labels for bilingual technical library records from title text alone, reaching 20.30% recall but only 2.84% precision on the shared task's test set.

desk verdict A modest SemEval system paper with an unablated capability claim and inconsistent qualitative numbers; the reported F1 of 4.66 is real but over-interpreted. read the letter →

arxiv 2504.21474 v1 pith:YNUOZO7C submitted 2025-04-30 cs.CL cs.AI

Hadi Bayrami Asl Tekanlou , Jafar Razmara , Mahsa Sanaei , Mostafa Rahgouy , Hamed Babaei Giglou This is my paper · ORCID

classification cs.CLcs.AI

keywords subjecttaggingontologyalignmentretrieval-augmentedgenerationGNDtaxonomymultilingualrecordssemantictextualsimilaritycontrastivelearninglibrarycatalogindexing

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 tries to show that a retrieval-augmented ontology-alignment pipeline can serve as a subject-candidate generator for bilingual library records, reading a title and returning ranked labels from the GND taxonomy. With only light fine-tuning of the retriever and a small language model, the system reaches an average recall of 20.30% on the shared task's evaluation set, while precision stays at 2.84%. The practical value would be a low-cost way for libraries to produce candidate subject tags for human review instead of tagging records from scratch. The authors frame the work as a case study of OntoAligner's flexibility for subject indexing, not as a finished automatic tagger.

What carries the argument

The load-bearing mechanism is OntoAligner's RAG alignment pipeline: a dense retriever built from a sentence-transformer-style Nomic embedding model, fine-tuned on contrastive semantic-textual-similarity pairs to score title–subject matches; top-$k=30$ candidate selection by cosine similarity; and a Qwen2.5-0.5B-Instruct LLM, fine-tuned with QLoRA, that reads each retrieved title–subject pair and decides whether it is a match. The modular design lets users fine-tune only the retriever and the LLM while the toolkit handles embedding storage, subject retrieval, and the alignment bookkeeping.

What would settle it

On the development set, replace title-only retrieval with title-plus-abstract retrieval and measure precision@30; if precision does not improve, the title-only assumption is not the main bottleneck, and the paper's choice of representation would need re-examination.

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Extended reading notes

Core claim

The central claim is that subject tagging can be reformulated as an ontology alignment problem: each library record is a source entity and each GND subject is a target entity, and alignment is performed by dense retrieval followed by an LLM judge. Using title-only representation, a contrastively fine-tuned Nomic-embed retriever returns the top $k=30$ subject candidates by cosine similarity, and a QLoRA-tuned Qwen2.5-0.5B model filters which candidates are genuine matches. On the shared task's test set this yields 20.30% recall and 2.84% precision, which the authors read as evidence that the pipeline captures a useful fraction of correct subjects while precision remains the main bottleneck. They also report that English records outperform German ones across all metrics and that articles and books are easier to tag than conference papers and reports.

Load-bearing premise

The pipeline assumes that a record's title alone, embedded into a vector space and matched to subject labels by cosine similarity, carries enough meaning to identify the correct subjects.

Editorial extensions

If this is right

Library catalogs could use this pipeline to pre-fill candidate GND subjects for human indexers, shrinking the search space from the full taxonomy to a short ranked list.
Because recall rises with $k$ while precision falls, the reported curves put the best recall–precision balance around $k=15$ to $k=20$, which is a natural operating point for review workflows.
English records are served noticeably better than German records, so improving the retriever and LLM for German is a concrete next step that follows from the paper's own results.
The pipeline transfers to other controlled vocabularies whenever record text and vocabulary labels can be embedded into the same vector space.
At 2.84% precision the system is not a reliable fully automatic tagger; its demonstrated role is candidate generation.

Reading between the lines

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

The paper does not report a retriever-only baseline, so it is an open question how much of the 20.30% recall comes from embedding similarity alone and how much from the Qwen filter; a simple ablation with $k=30$ and no LLM would settle it.
Because the authors chose title-only representation after unreported comparisons, a systematic dev-set comparison of title, title-plus-abstract, and parent-category inputs could show whether richer representations chiefly improve precision or recall.
The per-class recall pattern (strong on informatics and technology, weak on physics and mathematics) suggests a testable general hypothesis: ontology alignment works best for well-structured domains with clear boundaries between concepts.
The same alignment framing could be applied to other bilingual metadata tasks, such as mapping author-supplied keywords to controlled vocabularies, not just titles to GND subjects.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

This is a thin but honest system description for the SemEval-2025 subject tagging task. The new thing is applying OntoAligner, a published ontology alignment toolkit, to this specific task; the empirical results on TIB-Core-Subjects are new but incremental. The paper does a few things well: the pipeline is described cleanly, the fine-tuning setup is standard, and the authors are candid about the very low precision (2.84) and about the limitations of their small SFT dataset.

The main soft spot is the missing ablation. The pipeline is a cascade: dense retrieval of top-30 candidates, then a Qwen2.5-0.5B LLM filter. Without a retriever-only recall@30 baseline, or at least a random/frequency baseline over GND labels, there is no way to attribute the 20.30 recall to the RAG design. The paper's own Figure 2 even hints that ranking quality matters more than the LLM module, which undercuts the Section 5 claim that OntoAligner 'demonstrates its capability' and that the bottleneck is the small fine-tuning dataset. That attribution is unsupported as it stands.

There is also a concrete reporting error: Section 4.3 text gives Case 1 recall 24.26 and F1 20.06, while Table 2 gives 27.20 and 23.54; Case 2 is similarly off (19.55/13.63 vs 23.39/16.33). Those numbers need to be reconciled. Minor additional gaps: the choice of title-only representation over contextual or hierarchical is mentioned but the 'prior experimentation' is never shown, and no error bars or artifacts are released. These are not fatal—it is a system paper, not a scientific claim about the toolkit—but they are real weaknesses.

Who gets value: readers in digital libraries who want a quick sense of how a generic RAG pipeline behaves on subject tagging, and participants in the shared task. It will not change cataloging practice. The paper deserves a serious referee because it reports a complete evaluation on a shared task, but it needs a revision that adds a retriever-only baseline and fixes the Section 4.3 inconsistencies. I would accept it for peer review on that condition.

Referee Report

3 major / 6 minor

Summary. The manuscript describes Homa, the authors' submission to SemEval-2025 Task 5 for subject tagging of TIBKAT records against the GND taxonomy. The system treats the task as an ontology alignment problem using the OntoAligner toolkit: record titles are embedded with nomic-embed-text-v1, the top-30 GND subjects are retrieved by cosine similarity, and a QLoRA fine-tuned Qwen2.5-0.5B-Instruct validates the candidates. The retriever is fine-tuned with contrastive pairs and the LLM with supervised pairs. On TIB-Core-Subjects the system obtains average precision of 2.84%, recall of 20.30%, and F1 of 4.66, and qualitative results on two case studies are also reported. The authors conclude that OntoAligner can serve as a subject-candidate generator with minimal fine-tuning, while acknowledging that precision is low and that additional data and computational resources would be needed.

Significance. If the results were fully supported, the paper would provide a useful case study of applying a general ontology alignment toolkit to library subject indexing. The main contribution is an empirical demonstration that a RAG pipeline consisting of dense retrieval plus a small instruction-tuned LLM can propose relevant GND subjects for bilingual records, and the authors are honest about the low precision. The paper is less informative as a system comparison, however, because no baseline or ablation isolates the contribution of the LLM module, and the reported qualitative numbers are internally inconsistent. For a shared-task system description the significance is modest: the toolkit-integration narrative is useful, but the evidence base needs strengthening before the capability claim is fully supported.

major comments (3)

[§3.1–§4.2 and §5] The central capability claim that OntoAligner 'demonstrates its capability' as a subject candidate generator is not supported by any ablation or baseline. Because the pipeline first retrieves the top-30 candidates by dense retrieval and only then applies the LLM filter, the reported recall@30 of 20.30% could be produced almost entirely by the retriever alone, and the statement in §5 that the bottleneck is the small fine-tuning dataset is therefore unsubstantiated. Please add retriever-only recall@30, an unfine-tuned retriever baseline, a random or frequency-based GND-label baseline, and a comparison of LLM-filtered versus unfiltered candidates.
[§4.3 and Table 2] The qualitative results are internally inconsistent: the text reports Case 1 recall of 24.26% and F1 of 20.06%, while Table 2 reports 27.20% and 23.54% for the same case, and Case 2 similarly differs (19.55%/13.63% in the text versus 23.39%/16.33% in Table 2). These discrepancies must be corrected and the computation clarified, because they directly affect the reported evidence base and the performance-by-category discussion around Figure 4.
[§3.2] The choice of title-only input representation is load-bearing but undocumented. The authors state that prior experimentation on title, contextual, and hierarchical representations led them to prefer titles, yet no development-set numbers are reported and no comparison shows how much recall is lost by omitting abstracts, descriptions, and parent metadata. Because bilingual library records often require context beyond a title, please report the development-set results for all three representations or otherwise justify the choice quantitatively.

minor comments (6)

[Table 2] The caption says 'The averaged metrics are reported,' but it does not specify over which records or folds the averages are taken; please clarify.
[Figure 1] The x-axes of the development and test panels use different k ranges (5–20 for development and 5–30 for test), which makes cross-panel comparison confusing; please align the axes or explain why the ranges differ.
[§4.2] References such as 'according to the Figure 2 and Figure 1' are vague; please cite specific panels, as the figures contain multiple subplots.
[§4.2] The phrase 'with mostly having an upper quartile Q3 of 5' is unclear in both grammar and meaning; please state the quantity being summarized and its units.
[§4.3] The sentence reporting the Informatics recall contains a typo ('really') and appears to conflate Case 1 and Case 2; please correct it.
[General] The paper does not state whether code or model checkpoints will be released; for reproducibility, please include an availability statement.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper reports an external SemEval benchmark evaluation; self-citations are software dependencies and not load-bearing derivations.

full rationale

The paper's derivation chain is an empirical system description, not a formal derivation. The system uses the OntoAligner RAG pipeline (retriever plus LLM) with fine-tuned components, and is evaluated on the held-out SemEval-2025 TIB-Core-Subjects test set. The central result — recall 20.30%, precision 2.84%, F1 4.66 — is an externally benchmarked measurement, not a quantity derived from fitted constants or from the paper's own prior claims. The fine-tuning sets are built from training pairs and the evaluation is on distinct test records, so the results are not forced by construction. The self-citations to OntoAligner and LLMs4OM are software and method dependencies: using one's own toolkit as the experimental system is an application, not a proof-by-citation, and the paper does not invoke a self-authored uniqueness theorem to rule out alternatives. There is no equation in the paper that reduces a predicted quantity to an input by definition. The only reviewer concerns are evidentiary, not circular: no retriever-only ablation is reported, and Section 4.3's qualitative text numbers (24.26/20.06 and 19.55/13.63) disagree with Table 2 (27.20/23.54 and 23.39/16.33). These issues bear on robustness of the capability claim but do not make the claim circular.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new entities. Its central claim rests on standard RAG assumptions: correct training labels, title embeddings that capture relevance, and a small LLM capable of match judgment after SFT. The main hand-chosen hyperparameters are listed above; they are not fitted to the test set.

free parameters (5)

top_k = 30
Number of subject candidates retrieved per record; chosen by hand in Section 3.1 with no reported tuning.
retriever_negative_pairs_limit = 600
Maximum number of negative pairs per record in the retriever fine-tuning dataset to limit compute; chosen by hand in Section 3.2.
sft_pairs_limit = 200
Maximum number of samples per record in the LLM supervised fine-tuning dataset; chosen by hand in Section 3.2.
retriever_epochs = 3 epochs, batch size 32
Fine-tuning schedule for the sentence-transformer retriever; chosen by hand in Section 3.2.
llm_epochs = 10 epochs, batch size 8, QLoRA 8-bit
Fine-tuning schedule for Qwen2.5-0.5B-Instruct; chosen by hand in Section 3.2.

assumptions (4)

domain assumption The GND labels assigned to training records are correct ground truth.
Used to create positive and negative training pairs in Section 3.2 and to compute evaluation metrics in Section 4.2.
domain assumption Title-only text is sufficient to infer the correct GND subjects for a record.
Section 3.2 selects title-based representation, discarding contextual and hierarchical representations after unreported development experiments.
domain assumption Cosine similarity between Nomic embeddings captures cross-lingual semantic relevance between titles and subject labels.
This is the retrieval mechanism in Section 3.1 and is not validated against alternative embedding or retrieval methods.
domain assumption The fine-tuned Qwen2.5-0.5B model can reliably judge whether a retrieved candidate subject matches a record title.
The LLM filtering step in Section 3.1 depends on this ability; no analysis of filtering errors is provided.

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Cite this review

Pith. "Pith review of Homa at SemEval-2025 Task 5: Aligning Librarian Records with OntoAligner for Subject Tagging." pith.science (2026). https://pith.science/paper/YNUOZO7C

@misc{pith2026250421474,
  author       = {Pith},
  title        = {Pith review of: Homa at SemEval-2025 Task 5: Aligning Librarian Records with OntoAligner for Subject Tagging},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YNUOZO7C}},
  note         = {Machine review of arXiv:2504.21474}
}

read the original abstract

This paper presents our system, Homa, for SemEval-2025 Task 5: Subject Tagging, which focuses on automatically assigning subject labels to technical records from TIBKAT using the Gemeinsame Normdatei (GND) taxonomy. We leverage OntoAligner, a modular ontology alignment toolkit, to address this task by integrating retrieval-augmented generation (RAG) techniques. Our approach formulates the subject tagging problem as an alignment task, where records are matched to GND categories based on semantic similarity. We evaluate OntoAligner's adaptability for subject indexing and analyze its effectiveness in handling multilingual records. Experimental results demonstrate the strengths and limitations of this method, highlighting the potential of alignment techniques for improving subject tagging in digital libraries.

Figures

Figures reproduced from arXiv: 2504.21474 by the authors.

**Figure 2.** All the participant results on the test set. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Distribution of number of subjects across [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Recall analysis of qualitative results [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRING...
[2]

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" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...
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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.

[1] [1]

online" 'onlinestring :=

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRING...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

[3] [3]

Hamed Babaei Giglou, Jennifer D'Souza, Felix Engel, and S \"o ren Auer. 2025. Llms4om: Matching ontologies with large language models. In The Semantic Web: ESWC 2024 Satellite Events, pages 25--35, Cham. Springer Nature Switzerland

work page 2025

[4] [4]

Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, et al. 2024. A survey on evaluation of large language models. ACM transactions on intelligent systems and technology, 15(3):1--45

2024

[5] [5]

Christopher Cox and Elias Tzoc. 2023. Chatgpt: Implications for academic libraries. College & research libraries news, 84(3):99

work page 2023

[6] [6]

Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2023. Qlora: Efficient finetuning of quantized llms. Advances in neural information processing systems, 36:10088--10115

2023

[7] [7]

Jennifer D'Souza, Sameer Sadruddin, Holger Israel, Mathias Begoin, and Diana Slawig. 2025 a . https://sites.google.com/view/llms4subjects/home LLMs4Subjects 2025: Large Language Models for Subject Tagging . Accessed: 2025-02-21

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Jennifer D'Souza, Sameer Sadruddin, Holger Israel, Mathias Begoin, and Diana Slawig. 2025 b . https://aclanthology.org/2025.semeval2025-1.139 Semeval-2025 task 5: Llms4subjects - llm-based automated subject tagging for a national technical library's open-access catalog . In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-202...

work page 2025