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.
|Number of pages||12|
|Journal||IEEE Transactions on Knowledge and Data Engineering|
|Publication status||Published - 2019 Nov 1|
Bibliographical noteFunding Information:
This work was supported by a National Research Foundation of Korea (NRF) grant, which is funded by the Korean government. [MSIP(no. NRF-2016R1A2B4015873)].
© 2018 IEEE.
All Science Journal Classification (ASJC) codes
- Information Systems
- Computer Science Applications
- Computational Theory and Mathematics