# A Comparative Study of Question Answering over Knowledge Bases

Source: [A Comparative Study of Question Answering over Knowledge Bases](<https://www.sota2.com/research/paper/68fa6de51e209131cea3cbb2>)

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

Data updated: 2025-10-28T13:56:56.138Z

Authors: Khiem Vinh Tran, Hao Phu Phan, Khang Nguyen Duc Quach, Ngan Luu-Thuy Nguyen, Jun Jo, Thanh Tam Nguyen

Published: 2022-11-15T14:23:47.000Z

## Abstract

Question answering over knowledge bases (KBQA) has become a popular approach to help users extract information from knowledge bases. Although several systems exist, choosing one suitable for a particular application scenario is difficult. In this article, we provide a comparative study of six representative KBQA systems on eight benchmark datasets. In that, we study various question types, properties, languages, and domains to provide insights on where existing systems struggle. On top of that, we propose an advanced mapping algorithm to aid existing models in achieving superior results. Moreover, we also develop a multilingual corpus COVID-KGQA, which encourages COVID-19 research and multilingualism for the diversity of future AI. Finally, we discuss the key findings and their implications as well as performance guidelines and some future improvements. Our source code is available at \\url{https://github.com/tamlhp/kbqa}.

## 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/2211.08170>)

[PDF](<https://arxiv.org/pdf/2211.08170.pdf>)