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LLMs enhanced for schema-ontology mapping with self-demonstrations

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel self-demonstration approach to improve the effectiveness of large language models (LLMs) in schema-ontology mapping. This method combines neuro-symbolic task decomposition with automatically generated, pattern-guided demonstrations. Experiments on the RODI benchmark demonstrated significant accuracy gains, outperforming existing LLM-based methods by 25 percentage points in F1 score. AI

IMPACT This research could lead to more accurate and efficient integration of heterogeneous databases, improving knowledge representation in enterprises.

RANK_REASON The cluster contains an academic paper detailing a new method for LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs enhanced for schema-ontology mapping with self-demonstrations

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The cluster contains an academic paper detailing a new method for LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Siddhesh Thombre, Manasi Patwardhan, Sunita Sarawagi ·

    Surprising Effectiveness of Self-Demonstrations in Enhancing Schema-Ontology Mapping with LLMs

    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 …

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