LAMP (LLM-Assisted Mapping Pipeline), a novel approach that leverages Large Language Models (LLMs) to assist the mapping process in OBDI, is introduced, demonstrating significant improvements in F1-scores–particularly in complex mapping scenario–compared to single-prompt approach.
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 the integration challenge of heterogeneous relational databases into a centralized ontology.
Siddhesh Thombre, Manasi S. Patwardhan, Sunita Sarawagi· 0 citations
Constructing typed, justified semantic links between ontologies is essential for enabling interoperability across heterogeneous and interdisciplinary knowledge domains. However, manually curating such links is difficult to scale. To address this challenge, we propose an end-to-end framework for ontology network constru...
This paper conducts a systematic empirical study of several state-of-the-art LLMs under different prompting strategies, isolating the role of structural guidance and contextual information in generating valid mappings and highlighting both the potential and the limitations of LLMs in structured semantic generation task...
BACKGROUND
The increasing availability of machine-readable research data has created a growing need for efficient large-scale data harmonization (DH). Although large language models (LLMs) show promise for reducing the time and labor required for DH, their effective integration into harmonization workflows remains an i...
Hyelee Kim, Shuang Liang, K. Lanier et al.· JMIR AI· 0 citations
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