In this work, we present an end-to-end Knowledge Graph Question Answering (KGQA) system named GETT-QA. GETT-QA uses T5, a popular text-to-text pre-trained language model. The model takes a question in natural language as input and produces a simpler form of the intended SPARQL query. In the simpler form, the model does not directly produce entity and relation IDs. Instead, it produces corresponding entity and relation labels. The labels are grounded to KG entity and relation IDs in a subsequent step. To further improve the results, we instruct the model to produce a truncated version of the KG embedding for each entity. The truncated KG embedding enables a finer search for disambiguation purposes. We find that T5 is able to learn the truncated KG embeddings without any change of loss function, improving KGQA performance. As a result, we report strong results for LC-QuAD 2.0 and SimpleQuestions-Wikidata datasets on end-to-end KGQA over Wikidata.

翻译：摘要：本文提出了一种名为GETT-QA的端到端知识图谱问答（KGQA）系统。该系统采用T5这一流行的文本到文本预训练语言模型作为核心。模型以自然语言问题作为输入，并生成目标SPARQL查询的简化形式。在简化形式中，模型不直接生成实体和关系ID，而是生成对应的实体和关系标签。这些标签随后在一个后续步骤中映射到知识图谱的实体和关系ID。为了进一步提升结果，我们引导模型为每个实体生成知识图谱嵌入的截断版本。截断的知识图谱嵌入有助于实现更精细的消歧搜索。我们发现，T5能够在无需修改损失函数的情况下学习截断的知识图谱嵌入，从而提升KGQA性能。最终，我们在LC-QuAD 2.0和SimpleQuestions-Wikidata数据集上，针对基于Wikidata的端到端KGQA任务报告了优越的结果。