Abstract
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.
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Acknowledgement
This work is supported by the National Natural Science Foundation of China (No. 61772249), and partly by the Natural Science Foundation of Liaoning Province, China (20170540418, 20180550995).
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Meng, X., Zhang, X., Li, L., Zhang, Q., Li, P. (2019). Top-k Spatial Keyword Quer with Typicality and Semantics. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11448. Springer, Cham. https://doi.org/10.1007/978-3-030-18590-9_21
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DOI: https://doi.org/10.1007/978-3-030-18590-9_21
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