Work

Context-Aware Few-Shot Learning SPARQL Query Generation from Natural Language on an Aviation Knowledge Graph

Year: 2025

Type: article

Abstract: Question answering over domain-specific knowledge graphs implies several challenges. It requires sufficient knowledge of the world and the domain to understand what is being asked, familiarity with the knowledge graph’s structure to build a correct query, and knowledge of the query language. However, mastering all of these is a time-consuming task. This work proposes a prompt-based approach that enables natural language to generate SPARQL queries. By leveraging the advanced language capabilities of large language models (LLMs), we constructed prompts that include a natural-language question, relevant contextual information from the domain-specific knowledge graph, and several examples of how the task should be executed. To evaluate our method, we applied it to an aviation knowledge graph containing accident report data. Our approach improved the results of the original work—in which the aviation knowledge graph was first introduced—by 6%, demonstrating its potential for enhancing SPARQL query generation for domain-specific knowledge graphs.

Source: Machine Learning and Knowledge Extraction

DOI: 10.3390/make7020052

Authors: Ines-Virginia Hernandez-Camero, Eva Garcia-Lopez, Antonio Garcia-Cabot, Sergio Caro-Alvaro

Institution: Universidad de Alcalá

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,172

Funders: Ministerio de Ciencia, Innovación y Universidades, Ministerio de Asuntos Económicos y Transformación Digital, Gobierno de España