Knowledge Graph Question Answering (KGQA) systems enable access to semantic information for any user who can compose a question in natural language. KGQA systems are now a core component of many industrial applications, including chatbots and conversational search applications. Although distinct worldwide cultures speak different languages, the number of languages covered by KGQA systems and its resources is mainly limited to English. To implement KGQA systems worldwide, we need to expand the current KGQA resources to languages other than English. Taking into account the recent popularity that Large-Scale Language Models are receiving, we believe that providing quality resources is key to the development of future pipelines. One of these resources is the datasets used to train and test KGQA systems. Among the few multilingual KGQA datasets available, only one covers Spanish, i.e., QALD-9. We reviewed the Spanish translations in the QALD-9 dataset and confirmed several issues that may affect the KGQA system’s quality. Taking this into account, we created new Spanish translations for this dataset and reviewed them manually with the help of native speakers. This dataset provides newly created, high-quality translations for QALD-9; we call this extension QALD-9-ES. We merged these translations into the QALD-9-plus dataset, which provides trustworthy native translations for QALD-9 in nine languages, intending to create one complete source of high-quality translations. We compared the new translations with the QALD-9 original ones using language-agnostic quantitative text analysis measures and found improvements in the results of the new translations. Finally, we compared both translations using the GERBIL QA benchmark framework using a KGQA system that supports Spanish. Although the question-answering scores only improved slightly, we believe that improving the quality of the existing translations will result in better KGQA systems and therefore increase the applicability of KGQA w.r.t. the Spanish language domain.