Semantic parsing-based methods for knowledge base question answering have achieved leading performance, but rely on high-quality training data of question-SPARQL query pairs, which requires substantial manual effort. In contrast, question-answer pairs are much easier to obtain. In this paper, we propose a novel task, namely SPARQL Query Annotating (SQA), to automatically construct queries with given question-answer pairs. We propose a novel metric test suite score for the evaluation of this task, and collect a dataset ReQuMA to evaluate this task’s facilitation on manual annotation. We also present QuAD, a Question-Answer Driven method for this task. Our experiments show that the task effectively eases manual annotation, and helps semantic parsing-based KBQA methods to maintain competitive performance when using the constructed queries from QuAD as training data. Analysis shows our test suite score metric effectively reflects the quality of constructed queries. Meanwhile, the substantial performance of QuAD demonstrates its effectiveness as a dedicated SQA method. Our code is available at https://github.com/nju-websoft/SQA .