The paper introduces Class Expression Simplifier (CES), an algorithm that syntactically simplifies OWL class expressions in Description Logics. CES preserves formal semantics by applying rewriting rules to remove redundancies and produce simpler, equivalent expressions, resulting in more compact and human-readable representations. Experiments on two medium-sized ontologies show that CES improves reasoning efficiency and reduces verbosity, and the tool is available as an open-source Python package within OWLAPY.
Description logic programs are a powerful formalism for combining rules with ontologies. The well-supported semantics for description logic programs ensures that no answer sets rely on cyclic dependencies.
arXiv:2607. 21203v1 Announce Type: new Abstract: Description logic programs are a powerful formalism for combining rules with ontologies.
arXiv:2608. 06399v1 Announce Type: cross Abstract: Datalog rules are often used to define ontologies over Knowledge Graphs.
The paper introduces GLaMoR, a reasoning pipeline that converts OWL ontologies into graph-structured data and applies a Graph Language Model (GLM) for consistency checking. It addresses the challenge of verifying ontology consistency, especially for large ontologies where classical reasoners become computationally expensive. Experiments on NCBO BioPortal ontologies show that GLaMoR achieves 95% accuracy and is 20 times faster than traditional reasoners.
arXiv:2608. 14104v1 Announce Type: cross Abstract: The Shapes Constraint Language (SHACL) is a W3C recommendation to express syntactic constraints, called shapes, on RDF graphs.
The OWL 2 EL profile is used in some of the largest production ontologies, including the Gene Ontology and SNOMED CT. Existing neuro-symbolic (NeSy) learning methods accept propositional theories or Datalog, and reasoning-shortcut (RS) awareness has not been investigated in ontology settings.
arXiv:2607. 15776v1 Announce Type: new Abstract: OWL ontologies provide a formal knowledge representation framework that enables semantic reasoning, and have been widely adopted across domains such as healthcare and bioinformatics.
arXiv:2507. 21438v2 Announce Type: replace Abstract: Ontologies and knowledge graphs require continuous evolution to remain comprehensive and accurate, but manual curation is labor intensive.
arXiv:2608. 12961v1 Announce Type: new Abstract: The OWL 2 EL profile is used in some of the largest production ontologies, including the Gene Ontology and SNOMED CT.
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.
arXiv:2608. 12374v1 Announce Type: cross Abstract: Language models (LMs) struggle with logical tasks like reasoning on syllogisms.
arXiv:2607. 16715v1 Announce Type: new Abstract: We study Controlled Query Evaluation (CQE), a declarative approach to confidentiality-preserving data access, in the context of Description Logic (DL) ontologies, and for confidentiality policies expressed through Epistemic Dependencies (EDs).