YASK

Author:

Chen Lei1,Xu Jianliang1,Jensen Christian S.2,Li Yafei1

Affiliation:

1. Hong Kong Baptist University, Hong Kong, China

2. Aalborg University, Denmark

Abstract

With the proliferation of the mobile use of the web, spatial keyword query (SKQ) services are gaining in importance. However, state-of-the-art SKQ systems do not provide systematic functionality that allows users to ask why some known object is unexpectedly missing from a query result and do not provide an explanation for such missing objects. In this demonstration, we present a system called YASK, a wh<u>Y</u>-not question <u>A</u>nswering engine for <u>S</u>patial <u>K</u>eyword query services, that is capable of answering why-not questions posed in response to answers to spatial keyword top- k queries. Two explanation and query refinement models, namely preference adjustment and keyword adaption , are implemented in YASK. The system provides users not only with the reasons why desired objects are missing from query results, but provides also relevant refined queries that revive the expected but missing objects. This demonstration gives attendees hands-on experience with YASK through a map-based GUI interface in which attendees can issue spatial keyword queries, pose why-not questions, and visualize the results.

Publisher

VLDB Endowment

Subject

General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Systematic Review of Indexing Spatial Skyline Queries for Decision Support;International Journal of Decision Support System Technology;2022-01

2. Why Questions on Time-Aware Spatial Keyword Queries in Traffic Networks;IEEE Access;2022

3. Progressive approaches to flexible group skyline queries;Knowledge and Information Systems;2021-04-09

4. Location- and keyword-based querying of geo-textual data: a survey;The VLDB Journal;2021-03-30

5. On Nearby-Fit Spatial Keyword Queries;IEEE Transactions on Knowledge and Data Engineering;2020-11-01

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