Reverse top-k keyword-based location query (RTkKL), aims to find the maximum spatial region such that the query object is contained in the result of any top-k spatial keyword query with users’ queried keywords and any location in the region as arguments. Existing efforts on RTkKL find the objects in the Euclidean space. In this paper, we study the problem of reverse top-k keyword-based location query on road networks. We propose two methods. One is based on mark vertex, and the other is based on bisector. For the mark vertex based method, we identify the mark vertex according to the definition of RTkKL on road networks. Based on the mark vertex, we will get the mark segments in the result. For the bisector-based method, we find the border points for the query q and some objects. With Dijkstra algorithm, we start from the query point q. For each closed edge, whose two adjacent vertices have been extracted from the min heap, we would search the border points on the edge, and count the border points for the adjacent vertex. For each method, we propose effective pruning strategy to reduce the search range and computation cost. Finally, experiments demonstrate the efficiency of the proposed algorithm.

反向基于前k 个关键字的位置查询（RT k KL），旨在找到最大空间区域，使得查询对象包含在任何前k 个空间关键字查询的结果中，用户查询的关键字和任何位置在区域作为参数。现有的 RT k KL 工作是在欧几里得空间中找到对象。在本文中，我们研究了基于反向top-k关键字的道路网络位置查询问题。我们提出两种方法。一种基于标记顶点，另一种基于平分线。对于基于标记顶点的方法，我们根据道路网络上RTkKL的定义来识别标记顶点. 基于标记顶点，我们将得到结果中的标记段。对于基于平分线的方法，我们找到查询q和一些对象的边界点。使用 Dijkstra 算法，我们从查询点q 开始。对于从最小堆中提取了两个相邻顶点的每个闭合边，我们将搜索边上的边界点，并计算相邻顶点的边界点。对于每种方法，我们都提出了有效的剪枝策略，以减少搜索范围和计算成本。最后，实验证明了所提出算法的有效性。