Top-k Spatial Keyword Quer with Typicality and Semantics

This paper proposes a top-k spatial keyword querying approach which can expeditiously provide top-k typical and semantically related spatial objects to the given query. The location-semantic relationships between spatial objects are first measured and then the Gaussian probabilistic density-based estimation method is leveraged to find a few representative objects from the dataset. Next, the order of remaining objects in the dataset can be generated corresponding to each representative object according to the location-semantic relationships. The online processing step computes the spatial proximity and semantic relevancy between query and each representative object, and then the orders can be used to facilitate top-k selection by using the threshold algorithm. Results of preliminary experiments showed the effectiveness of our method.