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.

我们提出了一种简化的方法来增强多语种知识图谱问答系统，将语言模型的处理流程中直接融入语言上下文和实体信息。通过使用预训练的多语种转换器型语言模型来处理主要输入和辅助数据，我们的方法显著提高了语言模型将自然语言查询准确转换为相关SPARQL查询的能力。我们在最新的QALD数据集上展示了有希望的结果，包括QALD-9-Plus和QALD-10。此外，我们还在中文和日文上介绍并评估了我们的方法，从而扩大了现有数据集的语言多样性。

MST5——面向知识图谱的多语言问答