LATIDIA · Investigación

Eficacia sorprendente de las autodemostraciones en la mejora del mapeo de ontologías de esquemas con LLM

arXiv: 2609.13776v1Tipo de anuncio: nuevo Resumen: La integración de bases de datos relacionales heterogéneas en una ontología centralizada sigue siendo un desafío persistente en la representación del conocimiento empresarial, principalmente debido a la

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arXiv:2609.13776v1 Announce Type: new Abstract: Integrating heterogeneous relational databases into a centralized ontology remains a persistent challenge in enterprise knowledge representation, primarily due to semantic heterogeneity, cryptic schema naming, missing metadata, and the abstraction gap between relational schemas and ontological models. Although large language models (LLMs) offer strong semantic reasoning capabilities, we show that directly applying them through one-shot prompting or naive multi-stage pipelines leads to poor performance for schema-ontology mapping. This paper presents a self-demonstration-driven approach that combines a neuro-symbolic task decomposition with a novel mechanism for automatically generating pattern-guided, dependency-aware demonstrations to address this integration challenge. Our approach incorporates two key strategies to achieve substantial accuracy gains over