  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.
