I. Introduction
The core task of a Knowledge Graph Question Answering system is to represent a natural language question in the form of a structured query (e.g., SPARQL) to a knowledge graph (KG). In other words, KGQA systems provide access to the data in KGs via a natural-language user interface, s.t., end users are not required to learn a particular query language for fetching data manually. Obviously, the relevance (or accuracy) of the answers given by such system should strive to human performance and reduce labor costs for learning a particular query language; otherwise, the system is useless. Many researchers are aiming at measuring and increasing the Question Answering (QA) quality or the quality of a particular KGQA sub-tasks, such as named entity linking (e.g., [1]), expected answer type prediction (e.g., [2]), etc. However, the accessibility
The accessibility for the Web is defined by W3C: https://www.w3.org/standards/webdesign/accessibility
characteristic of the KGQA systems often stays overlooked. In this context, the perfect accessibility denotes an equivalent experience to all user groups of a particular KGQA system. Hence, such research questions as: “How many people can really take advantage of the high-quality KGQA system?” and “Who are these people?” as well as “How diverse they are?” are often left unnoticeable.