Proportionality in Spatial Keyword Search
Georgios Kalamatianos, Georgios John Fakas, Nikos Mamoulis · 2021
More often than not, spatial objects are associated with some context, in the form of text, descriptive tags (e.g. points of interest, flickr photos), or linked entities in semantic graphs (e.g. Yago2, DBpedia). Hence, location-based retrieval should be extended to consider not only the locations but also the context of the objects, especially when the retrieved objects are too many and the query result is overwhelming. In this paper, we study the problem of selecting a subset of the query result, which is the most representative. We argue that objects with similar context and nearby locations should proportionally be represented in the selection. Proportionality dictates the pairwise comparison of all retrieved objects and hence bears a high cost. We propose novel algorithms which greatly reduce the cost of proportional object selection in practice. Extensive empirical studies on real datasets show that our algorithms are effective and efficient. A user evaluation verifies that proportional selection is more preferable than random selection and selection based on object diversification.