Web网页中往往包含许多主题噪声,准确地自动抽取关键词成为技术难点。提出了一个文本对象网络模型DON,给出了对象节点的中心度概念和基于中心度的影响因子传播规则,并据此自动聚集DON中的主题社区(topic society),从而提高了模型的抗噪...Web网页中往往包含许多主题噪声,准确地自动抽取关键词成为技术难点。提出了一个文本对象网络模型DON,给出了对象节点的中心度概念和基于中心度的影响因子传播规则,并据此自动聚集DON中的主题社区(topic society),从而提高了模型的抗噪能力。提出一个基于DON的网页关键词自动抽取算法KEYDON(Keywords Extraction Algorithm Based on DON)。实验结果表明,与基于DocView模型的相应算法相比,KEYDON的准确率提高了近20%,这说明DON模型具有较强的抑制主题噪声能力。展开更多
Nowadays,cloud computing is used more and more widely,more and more people prefer to using cloud server to store data.So,how to encrypt the data efficiently is an important problem.The search efficiency of existed sea...Nowadays,cloud computing is used more and more widely,more and more people prefer to using cloud server to store data.So,how to encrypt the data efficiently is an important problem.The search efficiency of existed search schemes decreases as the index increases.For solving this problem,we build the two-level index.Simultaneously,for improving the semantic information,the central word expansion is combined.The purpose of privacy-preserving content-aware search by using the two-level index(CKESS)is that the first matching is performed by using the extended central words,then calculate the similarity between the trapdoor and the secondary index,finally return the results in turn.Through experiments and analysis,it is proved that our proposed schemes can resist multiple threat models and the schemes are secure and efficient.展开更多
文摘Web网页中往往包含许多主题噪声,准确地自动抽取关键词成为技术难点。提出了一个文本对象网络模型DON,给出了对象节点的中心度概念和基于中心度的影响因子传播规则,并据此自动聚集DON中的主题社区(topic society),从而提高了模型的抗噪能力。提出一个基于DON的网页关键词自动抽取算法KEYDON(Keywords Extraction Algorithm Based on DON)。实验结果表明,与基于DocView模型的相应算法相比,KEYDON的准确率提高了近20%,这说明DON模型具有较强的抑制主题噪声能力。
基金This work is supported by the National Natural Science Foundation of China under grant U1836110,U1836208,U1536206,61602253,61672294by the National Key R&D Program of China under grant 2018YFB1003205+5 种基金by China Postdoctoral Science Foundation(2017M610574)by the Jiangsu Basic Research Programs-Natural Science Foundation under grant numbers BK20181407by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fundby the Major Program of the National Social Science Fund of China(17ZDA092)Qing Lan Projectby the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)fund,China.
文摘Nowadays,cloud computing is used more and more widely,more and more people prefer to using cloud server to store data.So,how to encrypt the data efficiently is an important problem.The search efficiency of existed search schemes decreases as the index increases.For solving this problem,we build the two-level index.Simultaneously,for improving the semantic information,the central word expansion is combined.The purpose of privacy-preserving content-aware search by using the two-level index(CKESS)is that the first matching is performed by using the extended central words,then calculate the similarity between the trapdoor and the secondary index,finally return the results in turn.Through experiments and analysis,it is proved that our proposed schemes can resist multiple threat models and the schemes are secure and efficient.
