arXiv AI July 28, 2026 By Daniel Hern\'andez, Jong Hyun Jung, Yuji Ikeda, Yongliang Ou, Pranav Kumar, Tom Sch\"achtel, Wenchuan Liu, Xin Li, Xi Zhang, Xiang Xu, Lifang Zhu, Fritz K\"ormann, Steffen Staab, Blazej Grabowski

An Ontology for Machine Learning Interatomic Potentials

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arXiv:2607. 23219v1 Announce Type: new Abstract: Machine learning interatomic potentials (MLIPs) approximate quantum-mechanical energies and forces---conventionally computed by density functional theory (DFT) or wave-function methods---at a fraction of the cost.

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benchmarks

arXiv AI

Jul 3

OntoLearner: A Modular Python Library for Ontology Learning with Large Language Models

arXiv:2607. 01977v1 Announce Type: new Abstract: Ontology learning (OL) aims to automatically construct structured knowledge models from text, yet progress remains fragmented across methods, domains, and evaluation practices.

By Hamed Babaei Giglou, Jennifer D'Souza, Andrei Aioanei, Nandana Mihindukulasooriya, S\"oren Auer

llms benchmarks

arXiv AI

2d ago

Ontology-Grounded, Reasoner-Verified Benchmarks for Evaluating LLM Reasoning in Scientific AI

The paper introduces a pipeline that automatically creates ontology‑grounded multiple‑choice question benchmarks for evaluating large language models (LLMs) on logical reasoning tasks in scientific AI. By using OWL 2 ontologies, correct answers are guaranteed by design and distractors are generated and formally verified as incorrect through an OWL reasoner. Experiments on three ontologies—Pizza, PMDco, and DOID—yielded 112, 2,491, and 15,216 MCQs, respectively, with high natural‑language quality and challenging zero‑shot performance for six LLMs.

By Nishtha N. Vaidya, Stephan Grimm, Thomas Hubauer, Thomas A. Runkler

llms rag nlp benchmarks

arXiv AI

Sep 1

When Does Bigger Help? A Controlled Study of LLM Scale for Ontology Learning

arXiv:2608.31118v1 Announce Type: new Abstract: The effect of Large Language Model (LLM) scale on ontology learning (OL) performance remains insufficiently characterized. We present a controlled eval...

By Hamed Babaei Giglou, S\"oren Auer, Jennifer D'Souza

llms rag

arXiv AI

Jul 28

Retrieval-Augmented Generation of Ontologies from Relational Databases

arXiv:2506. 01232v2 Announce Type: replace-cross Abstract: Deriving OWL ontologies from relational database schemas supports semantic interoperability and downstream tasks such as knowledge graph population, ontology-based data access, graph-based learning, and automated reasoning.

By Nadeen Fathallah, Mojtaba Nayyeri, Athish A Yogi, Ratan Bahadur Thapa, Hans-Michael Tautenhahn, Anton Schnurpel, Steffen Staab

llms rag

arXiv AI

Aug 25

Toward Effective and Reliable LLM Agents via Dynamic Ontology

arXiv:2608.22974v1 Announce Type: new Abstract: Large language model (LLM) agents rely heavily on knowledge encoded in model parameters or presented as unstructured context. In domain-specific tasks,...

By Xiaohui Zhang, Zequn Sun, Chengyuan Yang, Yuanning Cui, Lingbing Guo, Wei Hu

llms agents

arXiv AI

Jul 24

Knowledge Graph Re-engineering Along the Ontological Continuum (extended version)

arXiv:2605. 22093v3 Announce Type: replace Abstract: Knowledge graphs have become the primary vehicle for data integration and are critical to the success of modern AI, but the diversity of KG modelling practices, from lightweight vocabularies to richly axiomatised ontologies, makes integration and reuse expensive and brittle.

By Enrico Daga, Valentina Tamma, Terry Payne

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OntoLearner: A Modular Python Library for Ontology Learning with Large Language Models

Ontology-Grounded, Reasoner-Verified Benchmarks for Evaluating LLM Reasoning in Scientific AI

When Does Bigger Help? A Controlled Study of LLM Scale for Ontology Learning

Retrieval-Augmented Generation of Ontologies from Relational Databases

Toward Effective and Reliable LLM Agents via Dynamic Ontology

Knowledge Graph Re-engineering Along the Ontological Continuum (extended version)