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

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成果类型:
期刊论文
作者:
Li, Yanhong;Zhang, Wang;Luo, Changyin;Du, Xiaokun;Li, Jianjun
通讯作者:
Changyin Luo
作者机构:
[Du, Xiaokun; Li, Yanhong; Zhang, Wang] South Cent Univ Nationalities, Coll Comp Sci, Wuhan, Peoples R China.
[Li, Jianjun; Luo, Changyin] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.
[Li, Jianjun] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan, Peoples R China.
通讯机构:
[Changyin Luo] S
School of Computer, Central China Normal University, Wuhan, China
语种:
英文
关键词:
Information filtering;Attribute matching;Augmented spatial keyword query;Keyword queries;Query refinement;Scoring functions;Similarity-matching;Spatial proximity;Textual similarities;Two-level partitioning;Why-not question;Query processing
期刊:
Knowledge-Based Systems
ISSN:
0950-7051
年:
2021
卷:
223
页码:
107047
基金类别:
National Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61309002, 61522208]; Science Foundation of Hubei Province, China [2017CFB135]; Ministry of education of Humanities and Social Science project, China [20YJCZH111]; Fundamental Research Funds for the Central Universities, ChinaFundamental Research Funds for the Central Universities [CCNU20ZT013]; Wuhan Science and Technology Program, China [2019010701011392]
机构署名:
本校为其他机构
院系归属:
计算机学院
摘要:
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, A(k)C, which adopts a two-level partitioning scheme, to efficiently organize the textual, attribute, and spatial information o...

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