GLaMoR: Consistency Checking of OWL Ontologies using Graph Language Models

Ansgar Scherp; Justin M\"ucke

REVIEW 4 major objections 4 minor 44 references

GLaMoR: Consistency Checking of OWL Ontologies using Graph Language Models

T0 review · 4 major / 4 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read An adapted Graph Language Model can classify OWL ontologies as consistent or inconsistent with 95% accuracy, and it does so far faster than a classical reasoner.

desk verdict A useful empirical study with a solid dataset and robustness analysis, but the headline accuracy claim over ModernBERT is within noise and needs statistical support. read the letter →

arxiv 2504.19023 v1 pith:P2TNDINX submitted 2025-04-26 cs.AI cs.LG

Justin Mücke , Ansgar Scherp This is my paper · ORCID

classification cs.AIcs.LG

keywords consistencycheckingOWLontologiesGraphLanguageModelsemanticreasoningclassificationanti-patterninjectionontologymodularization

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

This paper proposes GLaMoR, a pipeline that treats OWL ontology consistency checking as a binary graph classification problem instead of a purely logical one. Large ontologies are split into smaller modules, a balanced set of inconsistent examples is created by injecting logical anti-patterns, and each module is verbalized as English-like triples for a Graph Language Model. On a dataset built from a large biomedical ontology repository, the best model—a global-attention GLM with T5-base—reaches 95.13% accuracy and the highest precision among all compared models, beating classical machine-learning baselines and several modern language and graph models. The same model completes training and inference in under six hours, while the classical reasoner used for comparison spends 122 hours on the test set. If the result holds, learned models become a practical fast screening tool for ontology consistency, complementing or replacing reasoners where speed matters.

What carries the argument

The central object is the Graph Language Model: a Transformer whose attention is augmented with graph-structural positional encodings and initialized from T5 weights, allowing an ontology to be read as a graph rather than a flat string. Each OWL module is first converted into a Levi graph—one node per entity and per relation, with relation nodes connecting their head and tail—then into token sequences. The global-attention setting lets any node attend to any other node, while the local setting restricts attention to a single triple. Mean pooling over the model's output logits turns the sequence into a binary consistency score. The anti-pattern injection rules and the modularization step supply the training signal and keep inputs within the model's 4,096-token limit, but the GLM's graph-aware attention is the mechanism the paper credits for detecting contradictions spread across multiple axioms.

What would settle it

Run a standard OWL 2 reasoner over the 7,505 modules labeled consistent and see whether any are judged inconsistent; if even one is, the paper's ground truth is contaminated. As a second check, test the trained global GLM on naturally occurring inconsistent ontologies that were not built from the 14 injected anti-patterns and compare its labels with reasoner labels.

Watch

Extended reading notes

Core claim

The paper claims that a Graph Language Model initialized from T5 and augmented with graph positional encodings can classify OWL ontology modules as consistent or inconsistent when the ontology is serialized as a sequence of triples. On a balanced dataset built from modularized biomedical ontologies and 14 injected logical anti-patterns, the global GLM with T5-base reaches 95.13% accuracy, 96.10% precision, and 94.17% recall, the best accuracy and precision among all machine-learning baselines and better than local-attention GLMs. The paper also reports that the same model keeps accuracy above 90% when whole families of anti-patterns are withheld from training, while local models degrade sharply on cyclic patterns; the authors attribute this to global attention's ability to connect axioms across the entire graph. This is presented as evidence that consistency checking is not the exclusive domain of logical reasoners.

Load-bearing premise

The load-bearing premise is that all 7,505 modules produced by the modularization tool are truly consistent; the paper labels them consistent without running a reasoner over them, so if any contains a contradiction the ground-truth labels and all reported metrics are corrupted.

Editorial extensions

If this is right

Consistency checking can be framed as binary graph classification, so the same triple-based pipeline could be retrained for other ontology properties without a reasoner.
Global attention is doing the heavy lifting: local GLMs lag behind and drop sharply on cyclic inconsistencies, so any learned reasoner for ontologies should attend over the whole graph rather than per-axiom.
The best model's wall-clock advantage—under six hours of training plus minutes of inference versus 122 hours for the classical reasoner—makes learned consistency screening practical for bulk ontology repositories.
Withholding an inconsistency family from training still leaves the global GLM above 90% accuracy, suggesting the model learns general contradiction-detecting structure rather than memorizing the 14 injected patterns.

Reading between the lines

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

A fair deployment comparison would amortize the model's one-time training cost over many ontologies; the paper's runtime advantage is a bulk-processing advantage, not necessarily a single-ontology one.
Because the verbalized triples are English sentences, part of the GLM's accuracy may come from T5's language priors rather than from graph structure; ablating the translation step to compare raw triples against English would separate the two contributions.
The strong global-over-local gap suggests the model's attention patterns could be inspected to see whether it learns to trace multi-axiom contradiction chains, connecting this work to explainable reasoning in description logics.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

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

Quick take: this is a decent empirical paper with a real dataset contribution, but the headline 'outperforms all baselines' isn't backed by any significance testing. The 0.86 pp accuracy gap over ModernBERT (95.13 ± 1.10 vs 94.27 ± 2.09) is within sampling noise. The robustness study is the strongest part.

What's new: first application of Graph Language Models to OWL consistency checking; a new dataset of 19,902 synthetic inconsistent modules generated from 14 anti-patterns; and a robustness analysis that withholds entire inconsistency families. The code is public. Global GLM with T5-base reaches ~95% accuracy, which is far better than embedding-based classifiers (all below 62%), and the global setting clearly beats the local one. The robustness result—global GLMs stay above 90% accuracy when whole families like UE* or CSC are absent from training—is a genuine generalization test, not just a held-out split. That deserves credit.

Soft spots, in order of severity. First, the comparative claim. The paper says the global GLM 'outperforms all baseline models' in the abstract and discussion, but the difference from ModernBERT is not significant given n=5 runs and overlapping standard deviations. No paired test, no CI. This is a fixable but load-bearing flaw.

Second, the runtime comparison. They compare total ML runtime (training + inference) against HermiT's inference-only time. HermiT doesn't train, so the '20x faster' framing understates the cost of building the model. They are explicit about what they measure, but a reader could easily over-read it.

Third, the 4,096-token filter: they discard nearly half of the consistent and inconsistent modules. So the result only speaks to small ontology modules. They acknowledge this in future work.

Fourth, the consistent labels from OAPT are not verified with a reasoner. The stress-test note points out, correctly, that HermiT's perfect 100/100/100 on the test set corroborates the test labels, so this is mostly a potential noise source in training, not a fatal flaw. Still, worth asking for a verification step.

Fifth, the anti-patterns are synthetic; real-world inconsistencies may differ. They list this as a threat to validity.

Bottom line: the paper is worth engaging with. The dataset and robustness study are valuable, and the empirical gap to ModernBERT, while not significant, is plausible. A serious referee should ask for significance testing, a runtime breakdown, and ideally a verification of the consistent-module labels. I'd send it to review rather than desk reject.

Referee Report

4 major / 4 minor

Summary. The paper introduces GLaMoR, a pipeline that turns OWL ontologies from the NCBO BioPortal repository into modules using OAPT, generates inconsistent examples by injecting axioms from 14 anti-patterns, translates Manchester syntax into English triples, and trains T5-based Graph Language Models (GLMs) in local and global settings to classify module consistency. The authors compare against classical machine-learning baselines on OWL2Vec* embeddings, PRODIGY, WideMLP, ModernBERT, LongT5, Llama3 in a zero-shot setting, and the HermiT reasoner, reporting that the global GLM with T5-base achieves 95.13% accuracy while HermiT takes 122 hours on the test set. A robustness study withholds inconsistency families from training to gauge generalization, and the code is released.

Significance. If the reported results are statistically sound, the work is a useful step toward scalable approximate consistency checking and contributes a new dataset of ontology modules with injected inconsistencies. The manuscript is transparent in releasing code, reports five-run averages with standard deviations, and evaluates on a held-out test set, and the robustness study is a good generalization check. The main caveats are that the headline comparison with ModernBERT is not supported by significance testing, that the hyperparameter protocol appears to use the test split for model selection, and that the token-length filter discards a large share of the data without analysis of representativeness.

major comments (4)

[Section IV-C] The hyperparameter optimization protocol states that for several models the authors "used a (70, 15, 15) split, and chose the parameters for which the accuracy on the test split was the highest." If the final numbers in Table IV are measured on that same test split, the reported accuracy is optimistically biased and the comparison between models is invalid. Please clarify whether a separate validation split was actually used; if not, model selection and final evaluation must be separated (e.g., by selecting on the validation split or using nested cross-validation) and the tables recomputed.
[Section V, Table IV] No statistical test accompanies the claim that the global GLM "outperforms all baseline models" (Abstract and Section VI-A). The accuracy gap between gGLM-base (95.13 ± 1.10) and ModernBERT (94.27 ± 2.09) is 0.86 percentage points, which is within one standard deviation of both run sets; the precision gap (96.10 ± 1.97 vs 95.82 ± 2.31) is similarly small. Please add a paired significance test (e.g., Wilcoxon signed-rank over the five runs) or confidence intervals, and temper the comparative claims in the Abstract and Section VI-A accordingly.
[Section IV-A, Table III] The 4,096-token filter discards 3,336 of 7,505 consistent modules (44%) and 9,823 of 19,902 inconsistent modules (49%). The paper does not analyze whether accuracy and runtime generalize to the discarded larger modules; as it stands, the speed comparison with HermiT and the accuracy claims apply only to modules of at most 4,096 tokens. Please report performance as a function of module size or provide evidence that the filtered subset is representative of the full modularized corpus.
[Section IV-A] The 7,505 "consistent" modules produced by OAPT are not explicitly verified with a reasoner. Although HermiT's 100% accuracy on the test set in Table IV implies that the test labels are correct, the released dataset's training labels should be verified (or a statement added about whether and how verification was performed), because label noise in the training split would propagate to the reported robustness analysis and to the usability of the released dataset.

minor comments (4)

[Abstract, Section VI-A, Table IV] The claim "20 times faster" does not match the reported numbers: HermiT takes 122:24:32.42, while the slowest ML runtime in Table IV is about 4.75 hours for gGLM-base, giving a ratio of roughly 25.7. Please state the exact ratio or use the "under six hours" formulation consistently.
[Table V] The caption should state explicitly whether the test set still contains the withheld inconsistency family. The text implies that it does, but this is a critical detail for interpreting the robustness experiment.
[Section VI-A] The sentence "with only 3% of classifications of inconsistent ontologies that are actually consistent ontologies" is confusing; please rephrase in terms of the false-positive rate derived from precision (96.10% implies approximately 3.9% false positives).
[Various] Typos and formatting issues: "Graph Lanugage Model" in Section I, "Modularizaition" in Appendix A, and "i.¸ e." in Appendix D.

Circularity Check

1 steps flagged · score 6.0 of 10

Reported test accuracies are selected on the test split itself, making the headline leaderboard partially circular.

fitted input called prediction [Section IV-C, Hyperparameter Optimization]
"For the following models we also performed a grid search and used a (70, 15, 15) split, and chose the parameters for which the accuracy on the test split was the highest."

The 15% test split is used both to select hyperparameters and to compute the accuracies reported in Table IV. The configuration that is declared best is, by construction, the one with the highest accuracy on the test split; the reported test metric is therefore the same objective that drove model selection, not an independent estimate. This affects the neural baselines and the GLMs used in the central comparison, so the claim that gGLM-base 'outperforms all baseline models' rests in part on a leaderboard that was optimized against its own evaluation set.

full rationale

The only concrete circular step is the hyperparameter protocol in Section IV-C: for the neural models (WideMLP, ModernBERT, LongT5, and the GLMs), grid-search configurations were chosen by accuracy on the 15% test split, and the same test split then supplies the accuracies in Table IV. This is test-set fitting: the reported 'prediction' is the objective used for model selection, so the leaderboard is partly produced by construction rather than by independent evaluation. This bears directly on the central comparative claim (gGLM-base 95.13% vs ModernBERT 94.27%). I found no load-bearing self-citation circularity: the GLM architecture is cited to Plenz and Frank (different authors), the WideMLP citation includes coauthor Scherp but is only a baseline, and no uniqueness theorem is imported. The anti-pattern injection is a standard supervised setup with held-out modules and a robustness study that withholds entire inconsistency families, so it is not circular. The unverified 'consistent' module labels are a data-quality concern, not a circularity, and in any case do not reduce the derivation to its inputs. The architecture and task formulation have independent content, but the headline empirical comparison is partially circular because the test set was used for model selection; hence score 6.

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

The central claim is an empirical benchmark, so the free parameters are hyperparameters tuned on validation splits. The load-bearing assumptions are that OAPT modules are consistent as labeled, that the injected anti-patterns yield genuinely inconsistent OWL modules, and that the translation into English triples preserves enough logical structure. No new entities are introduced.

free parameters (4)

GLM learning rate = 1e-4
Selected by grid search; used for all GLM variants (Section IV-C).
GLM weight decay = 5e-4
Selected by grid search alongside learning rate (Section IV-C).
GLM class weight = 45
Tuned to favor the inconsistent class and effectively balance the classes (Section IV-C).
Token length cutoff = 4096
T5 maximum sequence length; filters the dataset to small modules and affects the reported accuracy (Section IV-D).

assumptions (3)

domain assumption Modules produced by OAPT from BioPortal ontologies are logically consistent as labeled.
The paper states 7,505 consistent modules were created but does not describe a consistency verification step (Section IV-A).
domain assumption Injecting the 14 anti-pattern axioms produces modules that are logically inconsistent under OWL 2 semantics.
Standard description logic semantics; the anti-patterns are from Corcho et al. [12] (Section III-B).
domain assumption The translation into English triples preserves enough logical structure for the classification task.
The paper removes prefixes and rewrites axioms as triples; no formal equivalence argument is given (Section III-C).

how reviews work

0 comments

Cite this review

Pith. "Pith review of GLaMoR: Consistency Checking of OWL Ontologies using Graph Language Models." pith.science (2026). https://pith.science/paper/P2TNDINX

@misc{pith2026250419023,
  author       = {Pith},
  title        = {Pith review of: GLaMoR: Consistency Checking of OWL Ontologies using Graph Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/P2TNDINX}},
  note         = {Machine review of arXiv:2504.19023}
}

abstract

Semantic reasoning aims to infer new knowledge from existing knowledge, with OWL ontologies serving as a standardized framework for organizing information. A key challenge in semantic reasoning is verifying ontology consistency. However, state-of-the-art reasoners are computationally expensive, and their efficiency decreases as ontology sizes grow. While classical machine learning models have been explored for consistency checking, they struggle to capture complex relationships within ontologies. Large language models (LLMs) have shown promising results for simple reasoning tasks but perform poorly on structured reasoning. The recently introduced Graph Language Model (GLM) offers a way to simultaneously process graph-structured data and text. This paper proposes GLaMoR (Graph Language Model for Reasoning), a reasoning pipeline that transforms OWL ontologies into graph-structured data and adapts the GLM architecture for consistency checking. We evaluate GLaMoR on ontologies from the NCBO BioPortal repository, converting them into triples suitable for model input. Our results show that the GLM outperforms all baseline models, achieving $95\%$ accuracy while being 20 times faster than classical reasoners. The Code is accessible under: https://github.com/JustinMuecke/GLaMoR

Figures

Figures reproduced from arXiv: 2504.19023 by the authors.

**Figure 2.** Distribution on the number of ontologies that can be separated into [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

44 extracted references · 37 canonical work pages

[1]

Owl 2: The next step for owl,

B. C. Grau, I. Horrocks, B. Motik, B. Parsia, P. Patel-Schneider, and U. Sattler, “Owl 2: The next step for owl,” Journal of Web Semantics , vol. 6, no. 4, pp. 309–322, 2008. Semantic Web Challenge 2006/2007

work page 2008
[2]

The even more irresistible sroiq.,

I. Horrocks, O. Kutz, and U. Sattler, “The even more irresistible sroiq.,” Kr, vol. 6, pp. 57–67, 2006

work page 2006
[3]

Hermit: An OWL 2 reasoner,

B. Glimm, I. Horrocks, B. Motik, G. Stoilos, and Z. Wang, “Hermit: An OWL 2 reasoner,” J. Autom. Reason. , vol. 53, no. 3, pp. 245–269, 2014

work page 2014
[4]

Comparison of reasoners for large ontologies in the OWL 2 EL profile,

K. Dentler, R. Cornet, A. ten Teije, and N. de Keizer, “Comparison of reasoners for large ontologies in the OWL 2 EL profile,” Semantic Web, vol. 2, no. 2, pp. 71–87, 2011

work page 2011
[5]

Fast approximate a-box consis- tency checking using machine learning,

H. Paulheim and H. Stuckenschmidt, “Fast approximate a-box consis- tency checking using machine learning,” in The Semantic Web. Latest Advances and New Domains - 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29 - June 2, 2016, Proceedings (H. Sack, E. Blomqvist, M. d’Aquin, C. Ghidini, S. P. Ponzetto, and C. Lange, eds.), vol. 96...

work page 2016
[6]

Deeppath: A rein- forcement learning method for knowledge graph reasoning,

W. Xiong, T. Hoang, and W. Y . Wang, “Deeppath: A rein- forcement learning method for knowledge graph reasoning,” CoRR, vol. abs/1707.06690, 2017

arXiv 2017
[7]

Incorporating graph attention mechanism into knowledge graph reasoning based on deep reinforce- ment learning,

H. Wang, S. Li, R. Pan, and M. Mao, “Incorporating graph attention mechanism into knowledge graph reasoning based on deep reinforce- ment learning,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (K. Inui, J. Jiang, V . Ng, a...

work page 2019
[8]

Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning,

R. Das, S. Dhuliawala, M. Zaheer, L. Vilnis, I. Durugkar, A. Krishna- murthy, A. Smola, and A. McCallum, “Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning,” in 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proc...

work page 2018

Show all 44 references

[9]

Multi-hop knowledge graph reasoning with reward shaping,

X. V . Lin, R. Socher, and C. Xiong, “Multi-hop knowledge graph reasoning with reward shaping,” 2018

2018
[10]

Do large language models latently perform multi-hop reasoning?,

S. Yang, E. Gribovskaya, N. Kassner, M. Geva, and S. Riedel, “Do large language models latently perform multi-hop reasoning?,” 2024

2024
[11]

Graph language models,

M. Plenz and A. Frank, “Graph language models,” in Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (L.-W. Ku, A. Martins, and V . Srikumar, eds.), (Bangkok, Thailand), pp. 4477–4494, Association for Computational ...

2024
[12]

Catalogue of anti- patterns for formal ontology debugging,

O. Corcho, C. Roussey, and L. M. Vilches-Bl ´azquez, “Catalogue of anti- patterns for formal ontology debugging,” 01 2009

2009
[13]

The regression analysis of binary sequences,

D. R. Cox, “The regression analysis of binary sequences,” Journal of the Royal Statistical Society: Series B (Methodological) , vol. 20, no. 2, pp. 215–232, 1958

1958
[14]

Random forests,

L. Breiman, “Random forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, 2001

2001
[15]

Comparing support vector machines with gaussian kernels to radial basis function classifiers,

B. Sch ¨olkopf, K. K. Sung, C. J. C. Burges, F. Girosi, P. Niyogi, T. A. Poggio, and V . Vapnik, “Comparing support vector machines with gaussian kernels to radial basis function classifiers,” IEEE Trans. Signal Process., vol. 45, no. 11, pp. 2758–2765, 1997

1997
[16]

Naive (bayes) at forty: The independence assumption in information retrieval,

D. D. Lewis, “Naive (bayes) at forty: The independence assumption in information retrieval,” in Machine Learning: ECML-98, 10th European Conference on Machine Learning, Chemnitz, Germany, April 21-23, 1998, Proceedings (C. Nedellec and C. Rouveirol, eds.), vol. 1398 of Lecture...

1998
[17]

Bag-of-words vs. graph vs. sequence in text classification: Questioning the necessity of text-graphs and the surprising strength of a wide MLP,

L. Galke and A. Scherp, “Bag-of-words vs. graph vs. sequence in text classification: Questioning the necessity of text-graphs and the surprising strength of a wide MLP,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long ...

2022
[18]

Prodigy: Enabling in-context learning over graphs,

Q. Huang, H. Ren, P. Chen, G. Kr ˇzmanc, D. Zeng, P. Liang, and J. Leskovec, “Prodigy: Enabling in-context learning over graphs,” 2023

2023
[19]

Smarter, better, faster, longer: A modern bidirectional encoder for fast, memory efficient, and long context finetuning and inference,

B. Warner, A. Chaffin, B. Clavi ´e, O. Weller, O. Hallstr ¨om, S. Taghadouini, A. Gallagher, R. Biswas, F. Ladhak, T. Aarsen, N. Cooper, G. Adams, J. Howard, and I. Poli, “Smarter, better, faster, longer: A modern bidirectional encoder for fast, memory efficient, and long cont...

2024
[20]

Longt5: Efficient text-to-text transformer for long sequences,

M. Guo, J. Ainslie, D. Uthus, S. Ontanon, J. Ni, Y .-H. Sung, and Y . Yang, “Longt5: Efficient text-to-text transformer for long sequences,” 2022

2022
[21]

Meta-llama-3-8b

M. AI, “Meta-llama-3-8b.” https://huggingface.co/meta-llama/ Meta-Llama-3-8B, 2024

2024
[22]

From shiq and rdf to owl: the making of a web ontology language,

I. Horrocks, P. F. Patel-Schneider, and F. van Harmelen, “From shiq and rdf to owl: the making of a web ontology language,” Journal of Web Semantics, vol. 1, no. 1, pp. 7–26, 2003

2003
[23]

Antoniou and F

G. Antoniou and F. v. Harmelen, Web Ontology Language: OWL, pp. 91–
[24]

Glimm and Y

B. Glimm and Y . Kazakov, Classical Algorithms for Reasoning and Ex- planation in Description Logics, pp. 1–64. Cham: Springer International Publishing, 2019

2019
[25]

Pellet: An OWL DL reasoner,

E. Sirin and B. Parsia, “Pellet: An OWL DL reasoner,” in Proceedings of the 2004 International Workshop on Description Logics (DL2004), Whistler, British Columbia, Canada, June 6-8, 2004 (V . Haarslev and R. M ¨oller, eds.), vol. 104 of CEUR Workshop Proceedings , CEUR- WS.org, 2004

2004
[26]

Rdfox: A highly-scalable rdf store,

Y . Nenov, R. Piro, B. Motik, I. Horrocks, Z. Wu, and J. Banerjee, “Rdfox: A highly-scalable rdf store,” in The Semantic Web - ISWC 2015 (M. Arenas, O. Corcho, E. Simperl, M. Strohmaier, M. d’Aquin, K. Srinivas, P. Groth, M. Dumontier, J. Heflin, K. Thirunarayan, and S. Staab,...

2015
[27]

Partitioning of bioportal ontologies: An empirical study.,

A. Algergawy and B. K ¨onig-Ries, “Partitioning of bioportal ontologies: An empirical study.,” in SWAT4HCLS, pp. 84–93, 2019

2019
[28]

A flexible partitioning tool for large ontologies,

A. Schlicht and H. Stuckenschmidt, “A flexible partitioning tool for large ontologies,” in 2008 IEEE / WIC / ACM International Conference on Web Intelligence, WI 2008, 9-12 December 2008, Sydney, NSW, Australia, Main Conference Proceedings, pp. 482–488, IEEE Computer Society, 2008

2008
[29]

OAPT: A tool for ontology analysis and partitioning,

A. Algergawy, S. Babalou, F. Klan, and B. K ¨onig-Ries, “OAPT: A tool for ontology analysis and partitioning,” in Proceedings of the 19th International Conference on Extending Database Technology, EDBT 2016, Bordeaux, France, March 15-16, 2016, Bordeaux, France, March 15-16, 2...

2016
[30]

Knowledge Graph Embeddings: Open Challenges and Opportunities,

R. Biswas, L.-A. Kaffee, M. Cochez, S. Dumbrava, T. E. Jendal, M. Lis- sandrini, V . Lopez, E. L. Menc ´ıa, H. Paulheim, H. Sack, E. K. Vakaj, and G. de Melo, “Knowledge Graph Embeddings: Open Challenges and Opportunities,” Transactions on Graph Data and Knowledge , vol. 1, no...

2023
[31]

Translating embeddings for modeling multi-relational data,

A. Bordes, N. Usunier, A. Garcia-Dur ´an, J. Weston, and O. Yakhnenko, “Translating embeddings for modeling multi-relational data,” in Pro- ceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2 , NIPS’13, (Red Hook, NY , USA), p. 278...

2013
[32]

Embedding entities and relations for learning and inference in knowledge bases,

B. Yang, W. tau Yih, X. He, J. Gao, and L. Deng, “Embedding entities and relations for learning and inference in knowledge bases,” 2015

2015
[33]

Convolutional 2d knowledge graph embeddings,

T. Dettmers, P. Minervini, P. Stenetorp, and S. Riedel, “Convolutional 2d knowledge graph embeddings,” 2018

2018
[34]

Rdf2vec: Rdf graph embeddings for data mining,

P. Ristoski and H. Paulheim, “Rdf2vec: Rdf graph embeddings for data mining,” in The Semantic Web – ISWC 2016 (P. Groth, E. Simperl, A. Gray, M. Sabou, M. Kr ¨otzsch, F. Lecue, F. Fl ¨ock, and Y . Gil, eds.), (Cham), pp. 498–514, Springer International Publishing, 2016

2016
[35]

Owl2vec*: Embedding of owl ontologies,

J. Chen, P. Hu, E. Jimenez-Ruiz, O. M. Holter, D. Antonyrajah, and I. Horrocks, “Owl2vec*: Embedding of owl ontologies,” 2021

2021
[36]

Towards graph foundation models: A survey and beyond,

J. Liu, C. Yang, Z. Lu, J. Chen, Y . Li, M. Zhang, T. Bai, Y . Fang, L. Sun, P. S. Yu, and C. Shi, “Towards graph foundation models: A survey and beyond,” 2024

2024
[37]

A generalization of transformer networks to graphs,

V . P. Dwivedi and X. Bresson, “A generalization of transformer networks to graphs,” 2021

2021
[38]

Exploring the limits of transfer learning with a unified text-to-text transformer,

C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y . Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” 2023

2023
[39]

Psp: Pre-training and structure prompt tuning for graph neural networks,

Q. Ge, Z. Zhao, Y . Liu, A. Cheng, X. Li, S. Wang, and D. Yin, “Psp: Pre-training and structure prompt tuning for graph neural networks,” 2024

2024
[40]

Graphany: A foundation model for node classification on any graph,

J. Zhao, H. Mostafa, M. Galkin, M. M. Bronstein, Z. Zhu, and J. Tang, “Graphany: A foundation model for node classification on any graph,” CoRR, vol. abs/2405.20445, 2024

2024 arXiv
[41]

Bioportal: enhanced functionality via new web services from the national center for biomedical ontology to access and use ontologies in software applications,

P. L. Whetzel, N. F. Noy, N. H. Shah, P. R. Alexander, C. Nyulas, T. Tudorache, and M. A. Musen, “Bioportal: enhanced functionality via new web services from the national center for biomedical ontology to access and use ontologies in software applications,” Nucleic Acids Resea...

2011
[42]

Efficient estimation of word representations in vector space,

T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” 2013

2013
[43]

A limited memory bfgs-type method for large-scale unconstrained optimization,

Y . Xiao, Z. Wei, and Z. Wang, “A limited memory bfgs-type method for large-scale unconstrained optimization,” Computers & Mathematics with Applications, vol. 56, no. 4, pp. 1001–1009, 2008. SUPPLEMENTARY MATERIALS APPENDIX A MODULARIZAITON Given the 1, 054 ontologies retrieve...

2008
[110]

Berlin, Heidelberg: Springer Berlin Heidelberg, 2009

2009

Pith tools

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

[1] [1]

Owl 2: The next step for owl,

B. C. Grau, I. Horrocks, B. Motik, B. Parsia, P. Patel-Schneider, and U. Sattler, “Owl 2: The next step for owl,” Journal of Web Semantics , vol. 6, no. 4, pp. 309–322, 2008. Semantic Web Challenge 2006/2007

work page 2008

[2] [2]

The even more irresistible sroiq.,

I. Horrocks, O. Kutz, and U. Sattler, “The even more irresistible sroiq.,” Kr, vol. 6, pp. 57–67, 2006

work page 2006

[3] [3]

Hermit: An OWL 2 reasoner,

B. Glimm, I. Horrocks, B. Motik, G. Stoilos, and Z. Wang, “Hermit: An OWL 2 reasoner,” J. Autom. Reason. , vol. 53, no. 3, pp. 245–269, 2014

work page 2014

[4] [4]

Comparison of reasoners for large ontologies in the OWL 2 EL profile,

K. Dentler, R. Cornet, A. ten Teije, and N. de Keizer, “Comparison of reasoners for large ontologies in the OWL 2 EL profile,” Semantic Web, vol. 2, no. 2, pp. 71–87, 2011

work page 2011

[5] [5]

Fast approximate a-box consis- tency checking using machine learning,

H. Paulheim and H. Stuckenschmidt, “Fast approximate a-box consis- tency checking using machine learning,” in The Semantic Web. Latest Advances and New Domains - 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29 - June 2, 2016, Proceedings (H. Sack, E. Blomqvist, M. d’Aquin, C. Ghidini, S. P. Ponzetto, and C. Lange, eds.), vol. 96...

work page 2016

[6] [6]

Deeppath: A rein- forcement learning method for knowledge graph reasoning,

W. Xiong, T. Hoang, and W. Y . Wang, “Deeppath: A rein- forcement learning method for knowledge graph reasoning,” CoRR, vol. abs/1707.06690, 2017

arXiv 2017

[7] [7]

Incorporating graph attention mechanism into knowledge graph reasoning based on deep reinforce- ment learning,

H. Wang, S. Li, R. Pan, and M. Mao, “Incorporating graph attention mechanism into knowledge graph reasoning based on deep reinforce- ment learning,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (K. Inui, J. Jiang, V . Ng, a...

work page 2019

[8] [8]

Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning,

R. Das, S. Dhuliawala, M. Zaheer, L. Vilnis, I. Durugkar, A. Krishna- murthy, A. Smola, and A. McCallum, “Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning,” in 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proc...

work page 2018