The benefits of query-based KGQA systems for complex and temporal questions in LLM era

Artem Alekseev, Mikhail Chaichuk, Miron Butko, Alexander Panchenko, Elena Tutubalina +1 more
7/16/2025
cs.CLcs.LG

Official code: ar2max/NLDB-KGQA-System (2 stars, last commit 2025-07-04).

Abstract

Large language models excel in question-answering (QA) yet still struggle with multi-hop reasoning and temporal questions. Query-based knowledge graph QA (KGQA) offers a modular alternative by generating executable queries instead of direct answers. We explore multi-stage query-based framework for WikiData QA, proposing multi-stage approach that enhances performance on challenging multi-hop and temporal benchmarks. Through generalization and rejection studies, we evaluate robustness across multi-hop and temporal QA datasets. Additionally, we introduce a novel entity linking and predicate matching method using CoT reasoning. Our results demonstrate the potential of query-based multi-stage KGQA framework for improving multi-hop and temporal QA with small language models. Code and data: https://github.com/ar2max/NLDB-KGQA-System

View on arXivView PDF

Code Implementations(1)

ar2max/NLDB-KGQA-SystemOfficial100%

This repository contains the experimental code for the scientific paper "The benefits of query-based KGQA systems for complex and temporal questions in LLM era", submitted at NLDB 2025

20Shell, PythonMar 15, 20251 years ago

Similar Papers

KG-MuLQA: A Framework for KG-based Multi-Level QA Extraction and Long-Context LLM Evaluation

87% match1 repo22025

PGDA-KGQA: A Prompt-Guided Generative Framework with Multiple Data Augmentation Strategies for Knowledge Graph Question Answering

86% match2025

Diagnosing and Addressing Pitfalls in KG-RAG Datasets: Toward More Reliable Benchmarking

86% match2025

Knowledge Graph-extended Retrieval Augmented Generation for Question Answering

85% match2025

iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering

85% match1 repo22025

Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunities

85% match1 repo1632025

Cite this paper

@article{alekseev2025benefits,
  title  = {The benefits of query-based KGQA systems for complex and temporal questions in LLM era},
  author = {Artem Alekseev and Mikhail Chaichuk and Miron Butko and Alexander Panchenko and Elena Tutubalina and Oleg Somov},
  year   = {2025},
  eprint = {2507.11954},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2507.11954v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2507.11954.svg)](https://codeofpaper.com/paper/2507.11954)

Discussion