Multi-objective spatial keyword query aims to find a set of objects that are reasonably distributed in spatial, with all query objectives to be satisfied. However, existing approaches mainly take the coverage of query keywords into account, while leaving the semantics of the textual data to be largely ignored. This limits us to return those rational results that are synonyms but morphologically different. To address this problem, this paper studies the problem of multi-objective spatial keyword query with semantics, and targets to return the object set that is optimum regarding to both spatial proximity and semantic relevance. Specifically, we take advantage of the probabilistic topic model and locality sensitive hashing (LSH), so that all query objectives can be satisfied in terms of their semantics. Afterwards, a novel indexing structure called LIR-tree is designed to integrate the spatial and semantic information of all objects in a balanced way. On top of the LIR-tree, we further propose a distance-owner based query processing algorithm, which provides tight bounds to achieve superb pruning effect in the searching phase. To speed up the processing, a distance owners based replacement strategy can be used to conduct approximate querying more efficiently. Empirical study based on a real dataset demonstrates the good effectiveness and efficiency of our proposed algorithms.

中文翻译：

---

具有语义的多目标空间关键字查询：一种基于距离所有者的方法

多目标空间关键词查询的目的是找到一组在空间上分布合理、满足所有查询目标的对象。然而，现有方法主要考虑查询关键字的覆盖范围，而在很大程度上忽略了文本数据的语义。这限制了我们返回那些同义但形态不同的理性结果。针对这一问题，本文研究了语义多目标空间关键词查询问题，旨在返回空间邻近性和语义相关性均最优的对象集。具体来说，我们利用概率主题模型和局部敏感哈希（LSH），以便在语义方面满足所有查询目标。然后，一种称为 LIR 树的新型索引结构旨在以平衡的方式整合所有对象的空间和语义信息。在 LIR 树的基础上，我们进一步提出了一种基于距离所有者的查询处理算法，该算法提供了严格的边界以在搜索阶段实现极好的修剪效果。为了加快处理速度，可以使用基于距离所有者的替换策略来更有效地进行近似查询。基于真实数据集的实证研究证明了我们提出的算法的良好有效性和效率。为了加快处理速度，可以使用基于距离所有者的替换策略来更有效地进行近似查询。基于真实数据集的实证研究证明了我们提出的算法的良好有效性和效率。为了加快处理速度，可以使用基于距离所有者的替换策略来更有效地进行近似查询。基于真实数据集的实证研究证明了我们提出的算法的良好有效性和效率。