SSTKG: Simple Spatio-Temporal Knowledge Graph for Intepretable and Versatile Dynamic Information Embedding
CoRR(2024)
摘要
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.
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