# Integrating SPARQL and LLMs for Question Answering over Scholarly Data Sources

Source: [Integrating SPARQL and LLMs for Question Answering over Scholarly Data Sources](<https://www.sota2.com/research/paper/68fa75df1e209131cea6a5ad>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:43:37.123Z

Authors: Fomubad Borista Fondi, Azanzi Jiomekong Fidel, Gaoussou Camara

Published: 2024-09-11T14:50:28.000Z

## Abstract

The Scholarly Hybrid Question Answering over Linked Data (QALD) Challenge at the International Semantic Web Conference (ISWC) 2024 focuses on Question Answering (QA) over diverse scholarly sources: DBLP, SemOpenAlex, and Wikipedia-based texts. This paper describes a methodology that combines SPARQL queries, divide and conquer algorithms, and a pre-trained extractive question answering model. It starts with SPARQL queries to gather data, then applies divide and conquer to manage various question types and sources, and uses the model to handle personal author questions. The approach, evaluated with Exact Match and F-score metrics, shows promise for improving QA accuracy and efficiency in scholarly contexts.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2409.18969>)

[PDF](<https://arxiv.org/pdf/2409.18969.pdf>)