元搜索引擎排序方法建模与算法研究

元搜索引擎的核心技术是排序算法.在研究元搜索引擎排序算法的基础上,通过合理简化等一系列措施建立数学模型,抽象出元搜索引擎排序算法的基本框架.随后结合各成员搜索引擎的市场份额及各成员搜索引擎的搜索结果,给出了一种新的元搜素引擎排序算法.该算法简单且易于实现.     

基于行为统计的全文搜索引擎排序算法

首先介绍了搜索引擎的基本概念,并对各种类别的搜索引擎作了简单的介绍;接着介绍了全文搜索引擎的各方面技术以及排序技术对于全文搜索引擎的重要性,然后分别介绍了当前两种重要的排序算法：PageRank排序和HilITop排序,并根据这两种排序算法提出基于行为统计的排序算法,最后对基于行为统计的排序算法作出评价和总结。     

个性化元搜索引擎模型研究

在分析了传统搜索引擎以及现有元搜索引擎不足之后,提出了基于Agent的个性化元搜索引擎模型,与传统的元搜索引擎系统相比改进了系统的运行效率,同时给出了基于用户反馈的个性化QR-加权重排序算法,将检索结果的排序质量以及文档的相关度在重排序中加以考虑,与传统的线性重排序算法相比提高了查询的准确程度。     

基于本体的元搜索引擎的设计与实现

与荚丈元搜索引擎相比,中文元搜索引擎还存在一定的差距,现有的中文元搜索引擎在实现关键词的扩展方面有待提高.通过对本体及元搜索引擎技术的研究,提出并实现了基于本体的元搜索引擎系统,介绍了系统的工作原理,通过对现有的元搜索引擎排序算法的分析,对摘要排序算法进行了改进.最终对系统进行测试并对其结果进行分析,该系统实现了对关键词的同义词和英丈扩展查询,有效地提高了系统的查全率和查准率.     

一种元搜索主题偏好的排序算法

元搜索引擎并行地向各个成员搜索引擎发出请求,合并及处理所有成员引擎的返回结果。相对于传统搜索引擎,元搜索引擎具有更好的查全率但在结果相关度排序及查准率方面仍需要改善。就相关度排序及查准率方面的问题元搜索成员引擎对于各个不同主题具有不同的检索质量并就此提出一种基于主题偏好的排序方法。利用Beeferman聚类方法对检索主题划分,通过Borda排序算法对元搜索引擎获得条目进行基于主题的分类排序,以此来提高元搜索查询质量和改善用户体验。     

基于信息量与信息嫡的元搜索引擎排序算法研究

元搜索引擎集合了多个成员搜索引擎的结果,将结果进行一定的处理后再将处理后的结果返回给用户。其中对结果的重新排序直接影响到元搜索引擎的性能。基于通信领域上的信息量与信息熵提出一种计算结果相关度的算法——信息关联度IRD算法,再将算法进行特定的修正,并提出一种合并算法CombMul,将以上算法应用到元搜索引擎中,最终用MRR查准率来评价此方法。得到的MRR查准率数据表明,与广泛应用的Borda排序算法相比,IRD算法结果更为理想。     

个性化元搜索结果整合算法的研究

针对当前元搜索引擎存在的问题,提出一种个性化元搜索结果整合算法。首先对成员搜索引擎根据相应条件设定权值,对各成员搜索引擎得到的搜索结果按贡献量加权分块排序,根据用户检索词条与兴趣库和元搜索结果的文本相关度对块内搜索结果进行整合排序。实验结果表明,该算法能够满足不同用户的个性化需求,在保证了搜索结果查全率的同时提高了查准率,很大程度上改善了用户检索效果和效率。     

基于本体的元搜索引擎结果排序算法研究

介绍了本体及元搜索引擎技术,认为将本体概念引入到元搜索引擎系统中,能够实现基于同义词、近义词的查询.给出了一个基于本体的元搜索引擎系统体系结构,介绍了系统的工作原理,提出了一种改进的摘要排序算法,并重点介绍了该算法的实现.     

基于GA的专业元搜索引擎结果排序算法

针对元搜索引擎的查准率等问题,分析专业元搜索引擎返回结果的专业相关度问题,提出一种基于遗传算法的网页排序算法,讨论算法的原理及实现过程。在独立搜索引擎返回结果中,挖掘除位置信息以外的其他信息,利用遗传算法建立专业网页相关度模型,对网页的专业相关度进行计算。实验结果表明该算法效果较好。     

一种元搜索引擎的查询结果处理模型

为改进元搜索引擎查询速度慢、独立性差的缺点，本文设计了一个元搜索引擎的结果处理模型。该模型结合元搜索引擎的特点设计了一种4级结果集的结构，提高了元搜索引擎结果处理的效率。在结果提取部分提出了根据反馈信息自动调整权重的算法（FBWM），在没有人工干预的情况下自动监视各独立搜索引擎的性能变化并随之动态调整其权重。在结果排序部分，提出了改进的位置／全文排序法（IPFTS），在算法中引入了词条匹配等级的概念，不但能提高搜索结果和查询串相关度的精度，还能保证排名在前的搜索结果的URL的有效性。     

Preference relations based unsupervised rank aggregation for metasearch

Rank aggregation mechanisms have been used in solving problems from various domains such as bioinformatics, natural language processing, information retrieval, etc. Metasearch is one such application where a user gives a query to the metasearch engine, and the metasearch engine forwards the query to multiple individual search engines. Results or rankings returned by these individual search engines are combined using rank aggregation algorithms to produce the final result to be displayed to the user. We identify few aspects that should be kept in mind for designing any rank aggregation algorithm for metasearch. For example, generally equal importance is given to the input rankings while performing the aggregation. However, depending on the indexed set of web pages, features considered for ranking, ranking functions used etc. by the individual search engines, the individual rankings may be of different qualities. So, the aggregation algorithm should give more weight to the better rankings while giving less weight to others. Also, since the aggregation is performed when the user is waiting for response, the operations performed in the algorithm need to be light weight. Moreover, getting supervised data for rank aggregation problem is often difficult. In this paper, we present an unsupervised rank aggregation algorithm that is suitable for metasearch and addresses the aspects mentioned above.We also perform detailed experimental evaluation of the proposed algorithm on four different benchmark datasets having ground truth information. Apart from the unsupervised Kendall-Tau distance measure, several supervised evaluation measures are used for performance comparison. Experimental results demonstrate the efficacy of the proposed algorithm over baseline methods in terms of supervised evaluation metrics. Through these experiments we also show that Kendall-Tau distance metric may not be suitable for evaluating rank aggregation algorithms for metasearch.     

Effective rank aggregation for metasearching

Nowadays, mashup services and especially metasearch engines play an increasingly important role on the Web. Most of users use them directly or indirectly to access and aggregate information from more than one data sources. Similarly to the rest of the search systems, the effectiveness of a metasearch engine is mainly determined by the quality of the results it returns in response to user queries. Since these services do not maintain their own document index, they exploit multiple search engines using a rank aggregation method in order to classify the collected results. However, the rank aggregation methods which have been proposed until now, utilize a very limited set of parameters regarding these results, such as the total number of the exploited resources and the rankings they receive from each individual resource. In this paper we present QuadRank, a new rank aggregation method, which takes into consideration additional information regarding the query terms, the collected results and the data correlated to each of these results (title, textual snippet, URL, individual ranking and others). We have implemented and tested QuadRank in a real-world metasearch engine, QuadSearch, a system developed as a testbed for algorithms related to the wide problem of metasearching. The name QuadSearch is related to the current number of the exploited engines (four). We have exhaustively tested QuadRank for both effectiveness and efficiency in the real-world search environment of QuadSearch and also, using a task from the recent TREC-2009 conference. The results we present in our experiments reveal that in most cases QuadRank outperformed all component engines, another metasearch engine (Dogpile) and two successful rank aggregation methods, Borda Count and the Outranking Approach.     

Internet上有关元搜索引擎的研究与设计

概述了Internet上一种新形式——元搜索引擎的概念、构成及分类,对元搜索引擎和一般的搜索引擎进行了比较,联系当前国内外元搜索引擎的现状,分析了其意义,提出了基于Agent的元搜索引擎的设计方案,指出了有自己独特的检索方式和处理方式,突破了传统搜索的局限性,并且该形式尚有许多重要问题亟待解决。     

元搜索引擎系统合成算法的约束条件

合成是元搜索引擎系统中一个重要的技术问题.给出了搜索引擎和元搜索引擎的形式化定义,对各种可能的元搜索引擎合成类型进行了划分.在此基础上,提出了元搜索引擎合成的一般性约束条件以及针对特殊类型的特殊约束条件.这些约束条件为构造好的元搜索引擎合成策略提供了基本保障.     

搜索引擎技术的新发展—多元搜索引擎系统

在分析传统搜索引擎技术所存在不足的基础上，介绍了搜索引擎发展过程中出现的一种新技术-多元搜索引擎，深入分析了多元搜索引擎系统的组成结构。详细地考察了多元搜索引擎的发展状况，并对多元搜索引擎的系统指标进行了完整的评价。     

集成搜索引擎的文本数据库选择

用户需要检索的信息往往分散存储在多个搜索多个搜索引擎各自的数据库里,对普通用户而言,访问多个搜索引擎并从返回的结果中分辨出确实有网页是一件费时费力的工作,集成搜索引擎则可以提供给用户一个同时记问多个搜索引擎人集成环境,集成搜索引擎能将其接收到的用户查询提交给底层的多个搜索引擎进行搜索,作为一种搜索工具,集成搜索引擎具有如WEB查询覆盖面比传统引擎更大,引警有更好的可扩展性等优点,讨论了解决集成搜索引擎的数据库选择问题的多种技术,针对用户提交的查询要求,通过数据库选择可以选定最有可能返回有用信息的底层搜索引擎。     

A neural network-based intelligent metasearch engine

Determining the relevancy of web pages to a query term is basic to the working of any search engine. In this paper we present a neural network based algorithm to classify the relevancy of search results on a metasearch engine. The fast learning neural network technology used by us enables the metasearch engine to handle a query term in a reasonably short time and return the search results with high accuracy.     

Supporting metasearch with XSL

Metasearch engines offer better coverage and are more fault-tolerant and expandable than single search engines. A metasearch engine is required to post queries with and obtain retrieval results from several other Internet search engines. In this paper, we describe the use of the extensible style language (XSL) to support metasearches. We show how XSL can transform a query, expressed in XML, into different forms for different search engines. We show how the retrieval results could be transformed into a standard format so that the metasearch engine can interpret the retrieved data, filtering the irrelevant information (e.g. advertisement). The proposed structure treats the metasearch engine and the individual search engines as separate modules with a clearly defined communication structure through XSL. Thus, the system is more extensible than coding the structure and syntactic transformation processes. It allows other new search engines to be included just through plug-and-play, requiring only that the new transformation of XML for this search engine be included in the XSL.     

基于弱实时系统的元搜索引擎合成策略

本文根据弱实时系统理论提出一种新的元搜索引擎结果合成策略，与现有系统相比可以较好把握系统响应时间与准确率的平衡，提高元搜索引擎的搜索速度和性能，并具有进一步深入研究的价值。     

LESSON: A system for lecture notes searching and sharing over Internet

In this paper, we present a system LESSON for lecture notes searching and sharing, which is dedicated to both instructors and students for effectively supporting their Web-based teaching and learning activities. The LESSON system employs a metasearch engine for lecture notes searching from Web and a peer-to-peer (P2P) overlay network for lecture notes sharing among the users. A metasearch engine provides an unified access to multiple existing component search engines and has better performance than general-purpose search engines. With the help of a P2P overlay network, all computers used by instructors and students can be connected into a virtual society over the Internet and communicate directly with each other for lecture notes sharing, without any centralized server and manipulation. In order to merge results from multiple component search engines into a single ranked list, we design the RSF strategy that takes rank, similarity and features of lecture notes into account. To implement query routing decision for effectively supporting lecture notes sharing, we propose a novel query routing mechanism. Experimental results indicate that the LESSON system has better performance in lecture notes searching from Web than some popular general-purpose search engines and some existing metasearch schemes; while processing queries within the system, it outperforms some typical routing methods. Concretely, it can achieve relatively high query hit rate with low bandwidth consumption in different types of network topologies.