Semantic-aware spatial keyword search is an important technique for digital map services. However, existing indexing and search methods have limited pruning effect due to the high dimensionality in semantic space, causing query efficiency to be a serious issue. To handle this problem, this paper proposes a novel pivot-based hierarchical indexing structure S²R-tree to integrate spatial and semantic information in a seamless way. Instead of indexing objects in the original semantic space, we carefully design a space mechanism to transform the high dimensional semantic vectors to a low dimensional space, so that more effective pruning effect can be achieved. On top of the S²R-tree, an efficient query processing algorithm is further designed, which not only ensures efficient query processing by a set of theoretical bounds, but also returns accurate results despite of the indexing in the low dimensional space. Furthermore, we conduct extensive experiments to evaluate and compare our proposed and baseline methods.

中文翻译：

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S 2 R-tree：用于语义感知的空间关键字搜索的基于枢轴的索引结构

语义感知的空间关键字搜索是数字地图服务的一项重要技术。然而，由于语义空间的高维性，现有的索引和搜索方法具有有限的修剪效果，导致查询效率成为严重的问题。为了解决这个问题，本文提出了一种新颖的基于枢轴的分层索引结构S ² R-tree，以无缝方式集成空间和语义信息。我们精心设计了一种空间机制，将高维语义向量转换为低维空间，而不是在原始语义空间中为对象建立索引，从而可以实现更有效的修剪效果。在S ²之上R树进一步设计了一种有效的查询处理算法，该算法不仅可以确保在一组理论范围内进行有效的查询处理，而且即使在低维空间中建立索引也可以返回准确的结果。此外，我们进行了广泛的实验，以评估和比较我们提出的方法和基准方法。