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

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.

Keywords

• knowledge graphs
• SPARQL
• query generation
• prompting
• large language models