Question Answering Over Spatio-Temporal Knowledge Graph

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.

Counter-intuitive: Large Language Models Can Better Understand Knowledge Graphs Than We Thought

Although the method of enhancing large language models' (LLMs') reasoning ability and reducing their hallucinations through the use of knowledge graphs (KGs) has received widespread attention, the exploration of how to enable LLMs to integrate the structured knowledge in KGs on-the-fly remains inadequate. Researchers often co-train KG embeddings and LLM parameters to equip LLMs with the ability of comprehending KG knowledge. However, this resource-hungry training paradigm significantly increases the model learning cost and is also unsuitable for non-open-source, black-box LLMs. In this paper, we employ complex question answering (CQA) as a task to assess the LLM's ability of comprehending KG knowledge. We conducted a comprehensive comparison of KG knowledge injection methods (from triples to natural language text), aiming to explore the optimal prompting method for supplying KG knowledge to LLMs, thereby enhancing their comprehension of KG. Contrary to our initial expectations, our analysis revealed that LLMs effectively handle messy, noisy, and linearized KG knowledge, outperforming methods that employ well-designed natural language (NL) textual prompts. This counter-intuitive finding provides substantial insights for future research on LLMs' comprehension of structured knowledge.