Enhancing the Accessibility of Knowledge Graph Question Answering Systems through Multilingualization

There are around 7000 languages spoken today in the world, yet English dominates in many research communities e.g., Knowledge Graph Question Answering (KGQA). The goal of a KGQA system is to provide natural language access to a knowledge graph. While many research works aim to achieve the best possible QA quality over English benchmarks, only a small share of them additionally focus on providing an equivalent experience for the ones who use such systems (i.e., accessibility). To fill this research gap, we investigate the multilingual aspect of the accessibility, which enables speakers of different languages (including low-resource and endangered languages) to interact with KGQA systems with an equivalent efficiency.