The authors present their enhancements to spatial keyword queries by using probabilistic topic modeling to incorporate semantic information. The topic model is based on latent Dirichlet allocation (LDA), which performs a statistical analysis to derive the semantic relevance of a topic to the relevant words in a set of documents. While this approach improves the quality of the search results, it can greatly increase the search space in which spatial objects have to be located: the combination of spatial aspects (reflecting the location of relevant objects on a map of the real world) and topical aspects (reflecting the similarity of the relevant objects to the search terms) leads to a high-dimensional search space, often characterized as the "curse of dimensionality."
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