[1]MENG Xiangfu,LAI Zhenxiang,CUI Jiangyan.Cohesive group query approach for collective spatial keywords[J].CAAI Transactions on Intelligent Systems,2024,19(3):707-718.[doi:10.11992/tis.202211013]
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Cohesive group query approach for collective spatial keywords

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 3 Page number: 707-718 Column: 学术论文—自然语言处理与理解 Public date: 2024-05-05
Title:
Cohesive group query approach for collective spatial keywords
Author(s):
MENG Xiangfu;  LAI Zhenxiang;  CUI Jiangyan
School of Electronic and Information Engineering, Liaoning Technical University, Huludao 125105, China
Keywords:
collective spatial keyword query; cohesive group query; road network; social network; core-tree structure; road network index; sliding window; point of interest
CLC:
TP311
DOI:
10.11992/tis.202211013
Abstract:
Given a road network and a social network, the collective spatial keyword query aims to find a set of points of interest (POIs) in which the text information contains all query keywords close to the query location and with a small mutual distance. The query goal of the cohesive group is to identify a group of users that are closely connected geographically and socially, whereas the query purpose of the collective spatial keyword cohesive group is to determine a pair of optimally matched POI sets and user sets that satisfy the query requirements. To address this problem, a novel type of cohesive group query mode is proposed for collective spatial keywords. Initially, the candidate POI set is obtained through a fast greedy query process. Then, the core tree structure is used to store the results of (k,c)-core decomposition to improve the efficiency of cohesive group query and ensure that the query results can satisfy the social constraints among users and the spatial constraints among POIs simultaneously. The experiments conducted on real datasets show that the proposed method is one to two orders of magnitude faster than the query efficiency of the enumeration method, and the results exhibit high query accuracy.
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