Efficient keyword search over graph-structured data based on minimal covered r-cliques

作     者：Asieh GHANBARPOUR Khashayar NIKNAFS Hassan NADERI Asieh GHANBARPOUR;Khashayar NIKNAFS;Hassan NADERI

作者机构：School of Computer EngineeringIran University of Science and TechnologyTehran 13114-16846Iran Department of Computer EngineeringUniversity of Sistan and BaluchestanZahedan 98167-45845Iran 

出 版 物：《Frontiers of Information Technology & Electronic Engineering》 (信息与电子工程前沿（英文版）)

年 卷 期：2020年第21卷第3期

页      面：448-465页

核心收录：

学科分类：07[理学] 081203[工学-计算机应用技术] 08[工学] 070104[理学-应用数学] 0835[工学-软件工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Keyword search Graph mining Information retrieval Database Clique 

摘      要：Keyword search is an alternative for structured languages in querying graph-structured data.A result to a keyword query is a connected structure covering all or part of the queried *** textual coverage and structural compactness have been known as the two main properties of a relevant result to a keyword *** previous works examined these properties after retrieving all of the candidate results using a ranking function in a comparative ***,this needs a time-consuming search process,which is not appropriate for an interactive system in which the user expects results in the least possible *** problem has been addressed in recent works by confining the shape of results to examine their coverage and compactness during the ***,these methods still suffer from the existence of redundant nodes in the retrieved *** this paper,we introduce the semantic of minimal covered r-clique(MCCr)for the results of a keyword query as an extended model of existing *** propose some efficient algorithms to detect the MCCrs of a given *** algorithms can retrieve a comprehensive set of non-duplicate MCCrs in response to a keyword *** addition,these algorithms can be executed in a distributive manner,which makes them outstanding in the field of keyword *** also propose the approximate versions of these algorithms to retrieve the top-k approximate MCCrs in a polynomial *** is proved that the approximate algorithms can retrieve results in *** experiments on two real-world datasets confirm the efficiency and effectiveness of the proposed algorithms.