Abstract: Spatial keyword search finds the points-of-interest (POIs) which are not only relevant to users’ query intent, but also close to query location. Spatial keyword search has many important applications, such as map search. Previous methods for spatial keyword search have the limitation that they only consider textual relevance of POIs to query keywords, and neglect the semantics of queries. So these methods may not be able to return relevant results or return many irrelevant results. To address this problem, this paper introduces a semantic-enhanced spatial keyword search method, named S3(semantic-enhanced spatial keyword search). Given a query, S3 analyzes the semantics of the query keywords to measure semantic distances of POIs to the query. Then, it utilizes a novel POI ranking mechanism by combining both semantic and spatial distance for effective POI search. S3 has the following challenges. Firstly, S3 introduces knowledge bases to help capture query semantics and introduces a ranking scoring function that considers both semantic distance and spatial distance. Secondly, it calls for instant search on large-scale POI data sets. To address this challenge, we devise a novel index structure GRTree, and develop some effective pruning techniques based on this structure. The extensive experiments on a real dataset show that S3 not only produces high-quality results, but also has good efficiency and scalability.