Collective Keyword Query on a Spatial Knowledge Base

Xiongnan Jin, Sangjin Shin, Eunju Jo, Kyong Ho Lee

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


The conventional works on spatial keyword queries for a knowledge base focus on finding a subtree to cover all the query keywords. The retrieved subtree is rooted at a place vertex, spatially close to a query location and compact in terms of the query keywords. However, user requirements may not be satisfied by a single subtree in some application scenarios. A group of subtrees should be combined together to collectively cover the query keywords. In this paper, we propose and study a novel way of searching on a spatial knowledge, namely collective spatial keyword query on a knowledge base (CoSKQ-KB). We formalize the problem of CoSKQ-KB and design a baseline method for CoSKQ-KB (BCK). To further speed up the query processing, an improved scalable method for CoSKQ-KB (iSCK) is proposed based on a set of efficient pruning and early termination techniques. In addition, we conduct empirical experiments on two real-world datasets to show the efficiency and effectiveness of our proposed algorithms.

Original languageEnglish
Article number8478325
Pages (from-to)2051-2062
Number of pages12
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number11
Publication statusPublished - 2019 Nov 1

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

All Science Journal Classification (ASJC) codes

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


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