Learning knowledge graph (KG) embeddings is an emerging technique for a variety of downstream tasks such as summarization, link prediction, information retrieval, and question answering. However, most existing KG embedding models neglect space and, therefore, do not perform well when applied to (geo)spatial data and tasks. Most models that do consider space primarily rely on some notions of distance. These models suffer from higher computational complexity during training while still losing information beyond the relative distance between entities. In this work, we propose a location‐aware KG embedding model called SE‐KGE. It directly encodes spatial information such as point coordinates or bounding boxes of geographic entities into the KG embedding space. The resulting model is capable of handling different types of spatial reasoning. We also construct a geographic knowledge graph as well as a set of geographic query–answer pairs called DBGeo to evaluate the performance of SE‐KGE in comparison to multiple baselines. Evaluation results show that SE‐KGE outperforms these baselines on the DBGeo data set for the geographic logic query answering task. This demonstrates the effectiveness of our spatially‐explicit model and the importance of considering the scale of different geographic entities. Finally, we introduce a novel downstream task called spatial semantic lifting which links an arbitrary location in the study area to entities in the KG via some relations. Evaluation on DBGeo shows that our model outperforms the baseline by a substantial margin.

中文翻译：

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SE‐KGE：用于地理问答和空间语义提升的位置感知知识图嵌入模型

学习知识图（KG）嵌入是一种用于各种下游任务的新兴技术，例如摘要，链接预测，信息检索和问题解答。但是，大多数现有的KG嵌入模型都忽略了空间，因此在应用于（地理）空间数据和任务时效果不佳。确实考虑空间的大多数模型主要依赖于距离的某些概念。这些模型在训练过程中遭受较高的计算复杂性，同时仍然丢失实体之间相对距离以外的信息。在这项工作中，我们提出了一种称为SE‐KGE的位置感知KG嵌入模型。它直接将空间信息（例如点坐标或地理实体的边界框）编码到KG嵌入空间中。生成的模型能够处理不同类型的空间推理。我们还构建了一个地理知识图以及一组称为DBGeo的地理查询-答案对，以评估SE-KGE与多个基准相比的性能。评估结果表明，对于地理逻辑查询回答任务，SE‐KGE优于DBGeo数据集上的这些基准。这证明了我们的空间显式模型的有效性以及考虑不同地理实体规模的重要性。最后，我们介绍了一个新颖的下游任务，称为空间语义提升，通过某种关系将研究区域中的任意位置链接到KG中的实体。对DBGeo的评估表明，我们的模型大大优于基线。