Knowledge graphs (KGs) have been increasingly employed for link prediction and recommendation using real-world datasets. However, the majority of current methods rely on static data, neglecting the dynamic nature and the hidden spatio-temporal attributes of real-world scenarios. This often results in suboptimal predictions and recommendations. Although there are effective spatio-temporal inference methods, they face challenges such as scalability with large datasets and inadequate semantic understanding, which impede their performance. To address these limitations, this paper introduces a novel framework - Simple Spatio-Temporal Knowledge Graph (SSTKG), for constructing and exploring spatio-temporal KGs. To integrate spatial and temporal data into KGs, our framework exploited through a new 3-step embedding method. Output embeddings can be used for future temporal sequence prediction and spatial information recommendation, providing valuable insights for various applications such as retail sales forecasting and traffic volume prediction. Our framework offers a simple but comprehensive way to understand the underlying patterns and trends in dynamic KG, thereby enhancing the accuracy of predictions and the relevance of recommendations. This work paves the way for more effective utilization of spatio-temporal data in KGs, with potential impacts across a wide range of sectors.

翻译：知识图谱（KGs）在利用真实数据集进行链接预测和推荐任务中的应用日益广泛。然而，当前多数方法依赖于静态数据，忽视了真实场景中的动态特性及隐藏的时空属性，常导致预测和推荐结果欠佳。尽管存在有效的时空推理方法，但其在大规模数据集上的可扩展性不足和语义理解不够深入等问题，阻碍了性能提升。为解决这些局限，本文提出了一种新颖框架——简洁时空知识图谱（SSTKG），用于构建和探索时空知识图谱。为将时空数据融入知识图谱，该框架通过一种新型三步嵌入方法进行挖掘。生成的嵌入可应用于未来时序预测和空间信息推荐，为零售销量预测、交通流量预测等多类应用提供有价值的洞察。该框架提供了一种简洁而全面的途径，用于理解动态知识图谱中的潜在模式与趋势，从而提升预测准确性与推荐相关性。本研究为知识图谱中时空数据的更有效利用奠定了基础，有望在多个领域产生广泛影响。