Spatio-temporal knowledge graphs (STKGs) extend the concept of knowledge graphs (KGs) by incorporating time and location information. While the research community's focus on Knowledge Graph Question Answering (KGQA), the field of answering questions incorporating both spatio-temporal information based on STKGs remains largely unexplored. Furthermore, a lack of comprehensive datasets also has hindered progress in this area. To address this issue, we present STQAD, a dataset comprising 10,000 natural language questions for spatio-temporal knowledge graph question answering (STKGQA). Unfortunately, various state-of-the-art KGQA approaches fall far short of achieving satisfactory performance on our dataset. In response, we propose STCQA, a new spatio-temporal KGQA approach that utilizes a novel STKG embedding method named STComplEx. By extracting temporal and spatial information from a question, our QA model can better comprehend the question and retrieve accurate answers from the STKG. Through extensive experiments, we demonstrate the quality of our dataset and the effectiveness of our STKGQA method.

通过建立一个新的名为STComplEx的STKG嵌入方法，从问题中提取时空信息，我们的STCQA模型可以更好地理解问题，并从STKG中检索准确答案。通过广泛的实验，我们展示了我们数据集的质量和我们STKGQA方法的有效性。

时空知识图谱的问答