Answering why-not questions on top-k augmented spatial keyword queries

The top-k augmented spatial keyword query (TkASKQ) retrieves k objects with the highest scores based on a scoring function, which considers spatial proximity, textual similarity and attribute matching simultaneously. As far as we know, no work has been conducted on answering why-not questions on TkASKQ queries (WTkASKQ). This paper takes the first step to address WTkASKQ queries by adopting a Query Refinement model. Specifically, we propose a hybrid indexing structure, AkC, which adopts a two-level partitioning scheme, to efficiently organize the textual, attribute, and spatial information of objects. Based on AkC, several filtering strategies are proposed to prune unqualified objects for query processing. To limit the number of refined queries to be explored, we construct new refined queries by sequentially extracting new keywords and attribute–value pairs from missing objects and adding them to the original keyword and attribute–value sets, respectively, so as to efficiently obtain the best refined query with minimal modification cost. In addition, we discuss the applicability of the methods in handling why-not questions on augmented regional queries, ordinary top-k SKQ queries and complex scoring queries. Experimental result shows that our AkC-based method has higher query efficiency compared with other baseline methods. •We introduce numerical attributes in the form of boolean expressions into the why-not questions of SKQ queries, which is closer to the real application scenario and can better answer why-not questions in TkASKQ queries (WTkASKQ for short).•We design an efficient hybrid index called AkC. In AkC, the keywords, numerical attributes, and spatial information of objects are smartly organized. In addition, several lemmas are proposed to prune huge amounts of irrelevant objects for WTkASKQ queries. Based on AkC, an efficient query processing algorithm is also proposed.•We also discuss how to extend our method to handle Why-not Questions on regional queries, ordinary SkQ queries, and complex scoring queries.•Extensive experimental results on two real datasets show that the proposed method performs much better than its competitors, and can obtain the best refined query with the lowest modification cost.