Multilingual question answering systems for knowledge graphs – a survey

Abstract

This paper presents a survey on multilingual Knowledge Graph Question Answering (mKGQA). We employ a systematic review methodology to collect and analyze the research results in the field of mKGQA by defining scientific literature sources, selecting relevant publications, extracting objective information (e.g., problem, approach, evaluation values, used metrics, etc.), thoroughly analyzing the information, searching for novel insights, and methodically organizing them. Our insights are derived from 46 publications: 26 papers specifically focused on mKGQA systems, 14 papers concerning benchmarks and datasets, and 7 systematic survey articles. Starting its search from 2011, this work presents a comprehensive overview of the research field, encompassing the most recent findings pertaining to mKGQA and Large Language Models. We categorize the acquired information into a well-defined taxonomy, which classifies the methods employed in the development of mKGQA systems. Moreover, we formally define three pivotal characteristics of these methods, namely resource efficiency, multilinguality, and portability. These formal definitions serve as crucial reference points for selecting an appropriate method for mKGQA in a given use case. Lastly, we delve into the challenges of mKGQA, offer a broad outlook on the investigated research field, and outline important directions for future research. Accompanying this paper, we provide all the collected data, scripts, and documentation in an online appendix.