  Knowledge Graph Question Answering (KGQA) simplifies querying vast amounts of
knowledge stored in a graph-based model using natural language. However, the
research has largely concentrated on English, putting non-English speakers at a
disadvantage. Meanwhile, existing multilingual KGQA systems face challenges in
achieving performance comparable to English systems, highlighting the
difficulty of generating SPARQL queries from diverse languages. In this
research, we propose a simplified approach to enhance multilingual KGQA systems
by incorporating linguistic context and entity information directly into the
processing pipeline of a language model. Unlike existing methods that rely on
separate encoders for integrating auxiliary information, our strategy leverages
a single, pretrained multilingual transformer-based language model to manage
both the primary input and the auxiliary data. Our methodology significantly
improves the language model's ability to accurately convert a natural language
query into a relevant SPARQL query. It demonstrates promising results on the
most recent QALD datasets, namely QALD-9-Plus and QALD-10. Furthermore, we
introduce and evaluate our approach on Chinese and Japanese, thereby expanding
the language diversity of the existing datasets.
