Question Answering Over Spatio-Temporal Knowledge Graph
This work addresses a largely unexplored problem in AI for applications requiring spatio-temporal reasoning, such as logistics or historical analysis, though it is incremental as it builds on existing KGQA methods.
The authors tackled the problem of question answering over spatio-temporal knowledge graphs (STKGs) by introducing STQAD, a dataset of 10,000 natural language questions, and STCQA, a new approach that uses STComplEx embeddings to improve performance, as existing methods performed poorly on this dataset.
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.