Knowledge Graph Completion (KGC) is an essential application in the field of knowledge graphs (KGs) that attempts to fill in the missing information in the process of KG modelling. With the popularity of temporal knowledge graphs (TKGs), a wide range of techniques based on temporal knowledge graph completion (TKGC) have appeared, solving the issue of real-world knowledge with temporal properties. However, there is little study on KGC with spatiotemporal attributes, some real-world data include both spatial and temporal attributes. Effectively handling the completion of missing entities or predicates in spatiotemporal knowledge graphs (STKGs) is an important challenge. Our study fills the gap in knowledge completion techniques in the field of STKG. We present a model for completion based on the well-known tensor factorization canonical polyadic (CP) decomposition. It introduces temporal and spatial attributes into the decomposition vector to achieve entity and link predictions. We name it diachronic and transregional word embedding (DT-WE), which includes two different modules: the embedding framework and the scoring module. Firstly, send the vectors to the embedding framework to get the new vector representation, then, we send it to the scoring module to compute, and finally, the resulting values are added together to compute the prediction probability. We conducted extensive experiments on three real-world STKGs: YAGO10K, Wikidata40K and Opensky. The results indicate that the newly introduced spatiotemporal attributes not only improve accuracy in predicting entities and predicates compared to temporal models but also achieve state-of-the-art performance with lower spatial complexity.

中文翻译：

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通过历时和跨区域词嵌入完成时空知识图

知识图补全（KGC）是知识图谱（KG）领域的一个重要应用，试图填补知识图谱建模过程中缺失的信息。随着时态知识图（TKG）的流行，出现了各种基于时态知识图补全（TKGC）的技术，解决了具有时态属性的现实世界知识的问题。然而，关于具有时空属性的KGC的研究很少，一些现实世界的数据同时包含空间和时间属性。有效处理时空知识图（STKG）中缺失实体或谓词的补全是一个重要的挑战。我们的研究填补了 STKG 领域知识补全技术的空白。我们提出了一个基于众所周知的张量分解正则多元（CP）分解的完成模型。它将时间和空间属性引入到分解向量中以实现实体和链接预测。我们将其命名为历时跨区域词嵌入（DT-WE），它包括两个不同的模块：嵌入框架和评分模块。首先，将向量发送到嵌入框架以获得新的向量表示，然后将其发送到评分模块进行计算，最后将结果值相加以计算预测概率。我们对三个现实世界的 STKG 进行了广泛的实验：YAGO10K、Wikidata40K 和 Opensky。结果表明，与时间模型相比，新引入的时空属性不仅提高了预测实体和谓词的准确性，而且以较低的空间复杂度实现了最先进的性能。