检索结果-南通市图书馆

LionRank: lion algorithm-based metasearch engines for re-ranking of webpages

Science China(Information Sciences) 2018年第12期61卷 65-80页

作者： P.VIJAYA Satish CHANDER Waljat College of Applied Sciences

Due to the rapid growth of the web, the process of collecting the relevant web pages based on the user query is one of the major challenging tasks in recent days. Hence, it is very complicated for the users to know the most relevant information even though various search engines are widely employed. To deal with users' trouble in identifying the relevant information from the web, we have proposed a meta-lion search engine to capture and analyze the ranking scores of various search engines and thereby, generate the re-ranked score results. Accordingly, LionRank, a lion algorithm-based meta-search engine is proposed for the re-ranking of the web pages. Here, different features like text based, factor based, rank based and classifier based features are used by the underlying search engines. In classifier based feature extraction,we have used the fuzzy integrated extended nearest neighbor(FENN) classifier to include the semantics in feature extraction. Moreover, an intelligent re-ranking process is proposed based on the lion algorithm to fuse the features scores optimally. Finally, the results of the proposed LionRank is analyzed with the web page database collected through four benchmark queries, and the quantitative performance are analyzed using precision, recall, and F-score. From the results, we proved that the proposed LionRank obtained the maximum F-score of 81% as compared with that of existing search engines like QuadRank, Outrank, Google,Yahoo, and Bing.

关键词： web technology meta-search engine feature extraction lion algorithm FENN classifier

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The Result Integration Algorithm Based on Matching Strategy

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Wuhan University Journal of Natural Sciences 2006年第1期11卷 113-116页

作者： XU Jia-shu YE Zhi-qiang QIN Zheng Department of Computer Science and Technology Xi＇an Jiaotong University Xi＇an 710049 Shaanxi China School of Management Huazhong University of Science and Technology Wuhan 430074 Hubei China Daya Bay Nuclear Power Operations and Management Co. Ltd Shenzhen 518124 Guangdong China

The following paper provides a new algorithm： a result integration algorithm based on matching strategy. The algorithm extracts the title and the abstract of Web pages, calculates the relevance between the query string and the Web pages, decides the Web pages accepted, rejected and sorts them out in user interfaces. The experiment results in dieate obviously that the new algorithms improve the precision of meta-search engine. This technique is very useful to metasearch engine.

关键词： meta-search engine relevance Web page

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REsearch ON OPTIMIZING THE MERGING RESULTS OF MULTIPLE INDEPENDENT RETRIEVAL SYSTEMS BY A DISCRETE PARTICLE SWARM OPTIMIZATION

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Journal of Electronics(China) 2012年第1期29卷 111-119页

作者： XieXingsheng ZhangGuoliang XiongYan AutomationDepartment UniversityofScienceandTechnologyofChinaHefei230027China ComputerDepartment UniversityofScienceandTechnologyofChinaHefei230027China

The result merging for multiple Independent Resource Retrieval Systems (IRRSs), which is a key component in developing a meta-search engine, is a difficult problem that still not effectively solved. Most of the existing result merging methods, usually suffered a great influence from the usefulness weight of different IRRS results and overlap rate among them. In this paper, we proposed a scheme that being capable of coalescing and optimizing a group of existing multi-sources-retrieval merging results effectively by Discrete Particle Swarm Optimization (DPSO). The experimental results show that the DPSO, not only can overall outperform all the other result merging algorithms it employed, but also has better adaptability in application for unnecessarily taking into account different IRRS's usefulness weight and their overlap rate with respect to a concrete query. Compared to other result merging algorithms it employed, the DPSO's recognition precision can increase nearly 24.6%, while the precision standard deviation for different queries can decrease about 68.3%.

关键词： Multiple resource retrievals Result merging meta-search engine Discrete ParticleSwarm Optimization (DPSO)

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REsearch ON OPTIMIZING THE MERGING RESULTS OF MULTIPLE INDEPENDENT RETRIEVAL SYSTEMS BY A DISCRETE PARTICLE SWARM OPTIMIZATION

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Journal of Electronics(China) 2012年第Z1期 111-119页

作者： Xie Xingsheng Zhang Guoliang Xiong Yan* (Automation Department, University of Science and Technology of China, Hefei 230027, China) *(Computer Department, University of Science and Technology of China, Hefei 230027, China)

The result merging for multiple Independent Resource Retrieval Systems (IRRSs), which is a key component in developing a meta-search engine, is a difficult problem that still not effectively solved. Most of the existing result merging methods, usually suffered a great influence from the usefulness weight of different IRRS results and overlap rate among them. In this paper, we proposed a scheme that being capable of coalescing and optimizing a group of existing multi-sources-retrieval merging results effectively by Discrete Particle Swarm Optimization (DPSO). The experimental results show that the DPSO, not only can overall outperform all the other result merging algorithms it employed, but also has better adaptability in application for unnecessarily taking into account different IRRS’s usefulness weight and their overlap rate with respect to a concrete query. Compared to other result merging algorithms it employed, the DPSO’s recognition precision can increase nearly 24.6%, while the precision standard deviation for different queries can decrease about 68.3%.

关键词： Multiple resource retrievals Result merging meta-search engine Discrete Particle Swarm Optimization (DPSO)

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元搜索引擎研究综述

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图书馆学情报学 2009年第9期

作者：吴小兰汪琪

介绍元搜索引擎的起源与发展、基本原理和分类，重点从元搜索引擎关键技术的实现上阐述元搜索引擎研究的主要内容与进展，最后分析现有元搜索引擎存在的各种局限性，归纳出未来值得研究和探讨的若干方向。

关键词：元搜索引擎个性化信息检索 meta-search engine Personalization Information retrieval

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面向跨领域海量信息资源的元搜索引擎研究

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图书馆学情报学 2011年第8期

作者：朝乐门张勇邢春晓

跨领域海量信息资源搜索是从事交叉学科和跨学科研究的重要前提。元搜索引擎不仅可以避免独立搜索引擎覆盖面较低的局限性，而且还可以充分发挥不同独立搜索引擎之间的互补性。基于元搜索引擎的跨领域海量信息资源搜索需要解决四个关键... 详细信息

跨领域海量信息资源搜索是从事交叉学科和跨学科研究的重要前提。元搜索引擎不仅可以避免独立搜索引擎覆盖面较低的局限性，而且还可以充分发挥不同独立搜索引擎之间的互补性。基于元搜索引擎的跨领域海量信息资源搜索需要解决四个关键问题，即协助用户准确表达搜索意图、消除检索词的“一词多义”和“多词一义”现象、设计自动调整检索范围的机制以及发挥用户价值。面向跨领域海量信息资源的元搜索引擎采用多领域本体、语义Web和W eb2.0技术，较好地解决了元搜索引擎的上述四个关键问题。相对于传统元搜索引擎，面向跨领域海最信息资源的元搜索引擎在基本思路、框架设计、流程设计、关键技术四个方面发生了重要变化。

关键词：元搜索引擎海量信息资源多领域本体语义Web Web2.0 meta-search engine Information resource Multiple domain ontology Semanticweh Web2.0

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Pinkysearch:基于聚类的元搜索引擎

PinkySearch:基于聚类的元搜索引擎

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第二十二届中国数据库学术会议

作者：余晋邓志鸿田敬唐世渭北京大学视觉与听觉信息处理国家重点实验室北京大学信息科学技术学院网络分布式实验室

1引言据中国互联网信息中心2005年1月发布的《中国互联网络发展状况统计报告》,用户经常使用的网络服务中搜索引擎占65%。网络信息的迅速膨胀使得搜索引擎越来越重要。同时,由于网络上的信息量十分巨大,没有任何一个搜索引擎能够覆盖整... 详细信息

1引言据中国互联网信息中心2005年1月发布的《中国互联网络发展状况统计报告》,用户经常使用的网络服务中搜索引擎占65%。网络信息的迅速膨胀使得搜索引擎越来越重要。同时,由于网络上的信息量十分巨大,没有任何一个搜索引擎能够覆盖整个网络,因此用户常常需要使用多个搜索引擎。

关键词： meta-search engine Clustering Clustering evaluation

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