Question Answering Over Spatio-Temporal Knowledge Graph

Anonymous

TL;DR: We constructed a large-scale spatio-temporal question answering dataset and proposed a spatio-temporal question answering method.

Abstract: 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). We find that 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. Experimental results demonstrate the quality of our dataset and the effectiveness of our STKGQA method.

Paper Type: long

Research Area: Question Answering

Contribution Types: Publicly available software and/or pre-trained models, Data resources

Languages Studied: English