RDF data are traditionally accessed using structured query languages, such as SPARQL. However, this requires users to understand the language as well as the RDF schema. Keyword search on RDF data aims at relieving users from these requirements; users only input a set of keywords, and the goal is to find small RDF subgraphs that contain all keywords. At the same time, popular RDF knowledge bases also include spatial and temporal semantics, which opens the road to spatiotemporal-based search operations. In this work, we propose and study novel keyword-based search queries with spatial semantics on RDF data, namely kSP queries. The objective of the kSP query is to find RDF subgraphs which contain the query keywords and are rooted at spatial entities close to the query location. To add temporal semantics to the kSP query, we propose the kSPT query that uses two ways to incorporate temporal information. One way is considering the temporal differences between the keyword-matched vertices and the query timestamp. The other way is using a temporal range to filter keyword-matched vertices. The novelty of kSP and kSPT queries is that they are spatiotemporal-aware and that they do not rely on the use of structured query languages. We design an efficient approach containing two pruning techniques and a data preprocessing technique for the processing of kSP queries. The proposed approach is extended and improved with four optimizations to evaluate kSPT queries. Extensive empirical studies on two real datasets demonstrate the superior and robust performance of our proposals compared to baseline methods.

中文翻译：

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时空RDF数据的前k个相关语义位置检索

传统上，RDF数据是使用结构化查询语言（例如SPARQL）访问的。但是，这要求用户理解语言以及RDF架构。在RDF数据上进行关键字搜索的目的是使用户摆脱这些要求；用户仅输入一组关键字，目标是找到包含所有关键字的小型RDF子图。同时，流行的RDF知识库还包括时空语义，这为基于时空的搜索操作开辟了道路。在这项工作中，我们提出并研究RDF数据上具有空间语义的新颖的基于关键字的搜索查询，即k SP查询。k的目标SP查询用于查找包含查询关键字并且植根于靠近查询位置的空间实体的RDF子图。为了向k SP查询添加时间语义，我们提出了k SPT查询，该查询使用两种方式来合并时间信息。一种方法是考虑关键字匹配的顶点和查询时间戳之间的时间差异。另一种方法是使用时间范围来过滤关键字匹配的顶点。k SP和k SPT查询的新颖之处在于它们具有时空感知能力，并且不依赖于使用结构化查询语言。我们设计了一种有效的方法，其中包含两种修剪技术和一种用于处理k的数据预处理技术SP查询。所提出的方法通过四个优化来扩展和改进，以评估k个SPT查询。在两个真实数据集上的大量实证研究表明，与基准方法相比，我们的建议具有优越的性能。