AI research
The research paper "Probabilistic Ontology Alignment in RAG: Handling Ambiguity in Large--Scale Logic Reasoning" (arXiv:2605.06312), published on May 5, 2026, introduces a Bayesian framework to resolve schema conflicts in multi--source Retrieval--Augmented Generation. The authors propose a "Probabilistic Logic Layer" (PLL) that assigns confidence scores to ontological mappings, allowing the LLM to weight retrieved evidence based on its logical alignment with a core knowledge graph. This approach mitigates "semantic drift" in complex reasoning tasks, demonstrating a 35% improvement in multi--hop deduction accuracy across heterogeneous datasets. This development is particularly relevant for sectors requiring high--fidelity automated reasoning, such as legal compliance and clinical decision support.