arXiv Machine Learning By Justin M\"ucke, Ansgar Scherp

GLaMoR: Consistency Checking of OWL Ontologies using Graph Language Models

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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.

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arXiv AI
Aug 28

SymbolLKG: Towards Verifiable Logical Reasoning via Logical Knowledge Graph and Symbolic Solvers

The paper introduces SymbolLKG, a neuro-symbolic framework that combines a Logical Knowledge Graph (LKG) with dynamic solver routing to improve logical reasoning in large language models. The LKG represents logical rules and constraints as topological nodes, enabling explicit modeling of dependencies extracted from text. A Logic Router dispatches tasks to the most suitable symbolic engine, supported by a topology-aware hybrid retrieval mechanism, and the approach outperforms existing prompting and RAG baselines on logical reasoning benchmarks.

By Haizhao Fan, Yuchi Xiong, Jize Wang, Xinping Guan, Xinyi Le