Towards Why-Not Spatial Keyword Top-k Queries: A Direction-Aware Approach

Lei Chen, Yafei Li*, Jianliang XU, Christian S. Jensen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

With the continued proliferation of location-based services, a growing number of web-accessible data objects are geo-tagged and have text descriptions. An important query over such web objects is the direction-aware spatial keyword query that aims to retrieve the top-k objects that best match query parameters in terms of spatial distance and textual similarity in a given query direction. In some cases, it can be difficult for users to specify appropriate query parameters. After getting a query result, users may find some desired objects are unexpectedly missing and may therefore question the entire result. Enabling why-not questions in this setting may aid users to retrieve better results, thus improving the overall utility of the query functionality. This paper studies the direction-aware why-not spatial keyword top- k query problem. We propose efficient query refinement techniques to revive missing objects by minimally modifying users' direction-aware queries. We prove that the best refined query directions lie in a finite solution space for a special case and reduce the search for the optimal refinement to a linear programming problem for the general case. Extensive experimental studies demonstrate that the proposed techniques outperform a baseline method by two orders of magnitude and are robust in a broad range of settings.

Original languageEnglish
Pages (from-to)796-809
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume30
Issue number4
DOIs
Publication statusPublished - 1 Apr 2018

Scopus Subject Areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

User-Defined Keywords

  • direction-aware
  • query refinement
  • spatial keyword top-k query
  • Why-not question

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