mlsanityData to 14 Oct 2026

Investigating Language Model Capabilities to Represent and Process Formal Knowledge: A Preliminary Study to Assist Ontology Engineering

arXiv 2509.10249 ↗First posted 12 Sep 2025cs.AIcited by 2

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Abstract (arXiv)

Recent advances in Language Models (LMs) have failed to mask their shortcomings particularly in the domain of reasoning. This limitation impacts several tasks, most notably those involving ontology engineering. As part of a PhD research, we investigate the consequences of incorporating formal methods on the performance of Small Language Models (SLMs) on reasoning tasks. Specifically, we aim to orient our work toward using SLMs to bootstrap ontology construction and set up a series of preliminary experiments to determine the impact of expressing logical problems with different grammars on the performance of SLMs on a predefined reasoning task. Our findings show that it is possible to substitute Natural Language (NL) with a more compact logical language while maintaining a strong performance on reasoning tasks and hope to use these results to further refine the role of SLMs in ontology engineering.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 29 of its 39 references that are in the map. First posted on arXiv 12 Sep 2025.

Builds on

29 of 39 references.

References · 29

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  2. 2024Training Large Language Models to Reason in a Continuous Latent Spacecited by 797
  3. 2022Towards Reasoning in Large Language Models: A Surveycited by 596
  4. 2024Large Language Models: A Surveycited by 525
  5. 2022FOLIO: Natural Language Reasoning with First-Order Logiccited by 235
  6. 2023Evaluating Large Language Models: A Comprehensive Surveycited by 202
  7. 2023Large Language Models on Graphs: A Comprehensive Surveycited by 192
  8. 2024Do Large Language Models Latently Perform Multi-Hop Reasoning?cited by 169
  9. 2025LLM Post-Training: A Deep Dive into Reasoning Large Language Modelscited by 135
  10. 2024Faithful Logical Reasoning via Symbolic Chain-of-Thoughtcited by 100
  11. 2023Grammar Prompting for Domain-Specific Language Generation with Large Language Modelscited by 58
  12. 2025Logical Reasoning in Large Language Models: A Surveycited by 55
17 more
  1. 2025Empowering LLMs with Logical Reasoning: A Comprehensive Surveycited by 51
  2. 2025Reasoning Language Models: A Blueprintcited by 42
  3. 2022Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computingcited by 35
  4. 2025Towards Reasoning Ability of Small Language Modelscited by 29
  5. 2024Do Large Language Models Perform Latent Multi-Hop Reasoning without Exploiting Shortcuts?cited by 28
  6. 2025Ontology Generation using Large Language Modelscited by 25
  7. 2024Logical Consistency of Large Language Models in Fact-checkingcited by 15
  8. 2025Guiding Reasoning in Small Language Models with LLM Assistancecited by 14
  9. 2021DomiKnowS: A Library for Integration of Symbolic Domain Knowledge in Deep Learningcited by 12
  10. 2023DOLCE: A Descriptive Ontology for Linguistic and Cognitive Engineeringcited by 11
  11. 2024Playing Language Game with LLMs Leads to Jailbreakingcited by 11
  12. 2024Enhancing the Reasoning Capabilities of Small Language Models via Solution Guidance Fine-Tuningcited by 11
  13. 2025Logic.py: Bridging the Gap between LLMs and Constraint Solverscited by 9
  14. 2022Can Transformers Reason in Fragments of Natural Language?cited by 8
  15. 2022Evaluating Step-by-Step Reasoning through Symbolic Verificationcited by 5
  16. 2024Using Large Language Models for OntoClean-based Ontology Refinementcited by 3
  17. 2024Improving Ontology Requirements Engineering with OntoChat and Participatory Promptingcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026SEF-CLGC at SemEval-2026 Task 11: Logical Notation Impact on Language Model Performancecited by 1
  2. 2026Are you Talking Logic to Me? Assessing Language Models Syllogistic Reasoning Capabilitiescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Assessing the Capability of Large Language Models for Domain-Specific Ontology Generation
  2. 2025OntoURL: A Benchmark for Evaluating Large Language Models on Symbolic Ontological Understanding, Reasoning and Learning
  3. 2026Large Language Model for OWL Proofs
  4. 2024Reasoning about concepts with LLMs: Inconsistencies abound
  5. 2024LOGIC-LM++: Multi-Step Refinement for Symbolic Formulations
  6. 2026Ontology-Grounded, Reasoner-Verified Benchmarks for Evaluating LLM Reasoning in Scientific AI
  7. 2025Are LLMs Stable Formal Logic Translators in Logical Reasoning Across Linguistically Diversified Texts?
  8. 2023Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models