Medical Information Retrieval in Natural Language Using LLMs for SPARQL Query Generation

Charalampos Doulaverakis, Giannis Vassiliou, Stavroula Chatzinikolaou, Sotiris Batsakis, Nikolaos Papadakis, Grigoris Antoniou

Abstract: Large Language Models (LLMs) have been the focus of Artificial Intelligence (AI) research recently but evaluation of their performance demonstrated their limitations in various tasks requiring reasoning capabilities since responses of LLMs often contain erroneous answers and non existent facts. In this work we propose a solution to this problem by making use of Linked Open Data as a source of reliable information. Specifically, we propose an approach that leverages Large Language Models (LLM) in order to allow for automatic SPARQL query generation from natural language by either extracting classes and properties of the KG and match them with corresponding keywords in the user query or by providing example entries of the dataset to the LLM so that it can analyze its structure. Preliminary results demonstrate the potential of both methods.

External IDs:dblp:conf/iisa/DoulaverakisVCBPA25