共查询到20条相似文献,搜索用时 140 毫秒
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[目的/意义]查询式搜索适用于目标明确的提问应答式信息问题,探索式搜索更注重搜索过程的人机交互性、动态性与多面性,两者表现出不同的行为特征。作为搜索行为研究的基本问题之一,相关研究还比较缺乏。论文旨在探究查询式搜索与探索式搜索行为特征的差异,这对于信息搜索系统的功能优化以及指导用户高效获取信息都具有重要的实践意义。[方法/过程]论文以健康信息搜索为例,采用搜索行为实验的方法,通过对录屏数据的分析,从检索策略、学习行为、深度搜索和搜索绩效4个维度对两种搜索行为进行比较。[结果/结论]查询式搜索与探索式搜索在关键词变换数、访问网页数目等6个指标上存在显著性差异,在检索工具选择、查询串长度、搜索结果集的翻页和相关链接搜索4个指标上不存在显著性差异。 相似文献
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网络用户的查询与点击行为研究 总被引:5,自引:1,他引:4
本文描述"网络用户搜索中语言使用行为的实验研究"的第二部分工作,包括定性数据处理与分析,网络搜索式的句法、语义和构造,以及用户点击行为中的语言利用问题.在前文所述实验的基础上,使用内容分析法进行定性数据分析.研究结果显示:句法在用户构造检索式时影响较弱;修改与调整、缩检二者是主要的语义调整方法;检索式构造有3种方式,同时又受到既有检索结果的影响;标题、摘要和来源是用户判断网页相关的主要依据. 相似文献
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社会合作信息查寻与检索:机制、模型与特征 总被引:1,自引:0,他引:1
通过典型人肉搜索案例"辽宁女事件"的剖析,揭示人肉搜索的社会合作信息查寻与检索性质;基于该案例分析社会合作信息查寻与检索过程机制,抽象出社会合作查寻与检索的框架模型。概括总结了社会合作查寻与检索的基本特征:搜索主体的群体自主性,搜索对象的全面性,搜索过程的迭代反馈相干性和智能性,搜索结果的准确适用性,以及去中心性、共享性及自组织性,并进一步讨论了社会合作信息查寻与检索代表的两种搜索发展模式:以人际关系为基础的智能化搜索模式,以正反馈累积为动力的"再中心化"自组织模式。 相似文献
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针对Web学术信息搜索结果的无序性和纷杂性,提出一种遍历搜索结果概念格检索算法,将检索用户第一次检出的学术信息组织和聚类并形成Hasse图,以此为基础,进行二次检索,在搜索结果数目过于庞大的情况下,帮助用户缩小查找范围,更准确地检索出所需内容。 相似文献
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知识元搜索引擎:CNKI知识搜索平台 总被引:5,自引:0,他引:5
介绍了CNKI知识搜索平台的功能,重点阐述了它的特色,它从技术、资源方面实现了多种资源的整合、实现了实时的知识聚类、知识元搜索、提供多样化的搜索排序和知识元链接功能,大大优于现有的各种搜索引擎和检索平台。本文对CNKI知识搜索平台的功能进行了评价,认为CNKI知识搜索平台是基于对文献内容的搜索,弥补了搜索引擎及同类检索平台的不足,能够满足用户需求,代表了电子资源检索平台的发展方向。 相似文献
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基于Google学术搜索的引文检索研究 总被引:7,自引:1,他引:7
在分析搜索引擎新进展的基础上,简要介绍了Google学术搜索的基本情况,并从关键词检索、作者检索和学术高级检索等方面详细地介绍了Google学术搜索的引文检索新功能,指出了其不足之处,提出了一些改进其质量的措施。 相似文献
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文章介绍了如何利用FTP搜索引擎和FTP搜索软件,在教育网上检索FTP信息资源,并对所检索到的FTP信息资源的下载和利用的方法作了说明。 相似文献
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Internet已成为全球最丰富的数据源,数据类型繁杂且动态变化,如何从中快速准确地检索出用户所需要的信息是一个亟待解决的问题.传统的搜索引擎基于语法的方式进行搜索,缺乏语义信息,难以准确地表达用户的查询需求和被检索对象的文档语义,致使查准率和查全率较低且搜索范围有限.本文对现有的语义检索方法进行了研究,分析了其中存在的问题,在此基础上提出了一种基于领域的语义搜索引擎模型,结合语义Web技术,使用领域本体元数据模型对用户的查询进行语义化规范,依据领域本体模式抽取文档中的知识并RDF化,准确地表达了用户的查询语义和作为被查询对象的文档语义,可以大大提高检索的准确性和检索效率,详细地给出了模型的体系结构、基本功能和工作原理. 相似文献
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基于Linux和Myeclipse 8.6平台,应用Java语言和开源工具Lucene,采用流行框架Struts 2.2,Spring 3.0,Hibernate 3.6开发了合作检索引擎"天涯·比邻",该搜索引擎主要通过用户检索输入与检索历史及当前检索进程的语词相似度计算来识别潜在用户。从合作检索界面、合作检索实现过程和检索轨迹呈现3个方面对"天涯·比邻"搜索引擎进行详细说明,最后讨论本研究相对于一般搜索引擎后控词表机制的主要改进及进一步研究内容。 相似文献
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从桌面搜索工具、搜索引擎指南、目录和论著资源等方面入手,对国外现有论述搜索引擎的主要资源的种类、性能和特色进行了述评。在此基础上,推荐有关搜索引擎的最佳资源,为人们学习掌握搜索引擎的资源、搜索技巧、方法和优化检索策略提供参考资料和学习途径。 相似文献
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Most of the peer-to-peer search techniques proposed in the recent years have focused on the single-key retrieval. However, similarity search in metric spaces represents an important paradigm for content-based retrieval in many applications. In this paper we introduce an extension of the well-known Content-Addressable Network paradigm to support storage and retrieval of more generic metric space objects. In particular we address the problem of executing the nearest neighbors queries, and propose three different algorithms of query propagation. An extensive experimental study on real-life data sets explores the performance characteristics of the proposed algorithms by showing their advantages and disadvantages. 相似文献
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Most of the Peer-to-Peer search techniques proposed in the recent years have focused on the single-key retrieval. However, similarity search in metric spaces represents an important paradigm for content-based retrieval in many applications. In this paper we introduce an extension of the well-known Content-Addressable Network paradigm to support storage and retrieval of more generic metric space objects. In particular we address the problem of executing the nearest neighbors queries, and propose three different algorithms of query execution. An extensive experimental study on real-life data sets explores the performance characteristics of the proposed algorithms by showing their advantages and disadvantages. 相似文献
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The retrieval effectiveness of the underlying document search component of an expert search engine can have an important impact on the effectiveness of the generated expert search results. In this large-scale study, we perform novel experiments in the context of the document search and expert search tasks of the TREC Enterprise track, to measure the influence that the performance of the document ranking has on the ranking of candidate experts. In particular, our experiments show that while the expert search system performance is related to the relevance of the retrieved documents, surprisingly, it is not always the case that increasing document search effectiveness causes an increase in expert search performance. Moreover, we simulate document rankings designed with expert search performance in mind and, through a failure analysis, show why even a perfect document ranking may not result in a perfect ranking of candidate experts. 相似文献
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Google为用户提供了多种检索功能,但这些检索功能的性能如何却少见于相关文献中。作者对Google的两种较为独特的检索功能进行了评析,分别是相似检索和链接检索,并以普通检索作为评价的标准。本文采用了20个检索式和20个目标网页,对相似检索、链接检索和普通检索的检索性能差异性进行了评价。研究中主要利用单因素方差分析法和回归分析法。研究结果显示普通检索表现最好。研究成果能帮助用户对相似检索和链接检索有较为清晰的认识,并且可以了解Google在这两种检索功能上所表现出的不同的检索性能。 相似文献
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Fatemeh Lashkari Ebrahim Bagheri Ali A. Ghorbani 《Information processing & management》2019,56(3):733-755
Traditional information retrieval techniques that primarily rely on keyword-based linking of the query and document spaces face challenges such as the vocabulary mismatch problem where relevant documents to a given query might not be retrieved simply due to the use of different terminology for describing the same concepts. As such, semantic search techniques aim to address such limitations of keyword-based retrieval models by incorporating semantic information from standard knowledge bases such as Freebase and DBpedia. The literature has already shown that while the sole consideration of semantic information might not lead to improved retrieval performance over keyword-based search, their consideration enables the retrieval of a set of relevant documents that cannot be retrieved by keyword-based methods. As such, building indices that store and provide access to semantic information during the retrieval process is important. While the process for building and querying keyword-based indices is quite well understood, the incorporation of semantic information within search indices is still an open challenge. Existing work have proposed to build one unified index encompassing both textual and semantic information or to build separate yet integrated indices for each information type but they face limitations such as increased query process time. In this paper, we propose to use neural embeddings-based representations of term, semantic entity, semantic type and documents within the same embedding space to facilitate the development of a unified search index that would consist of these four information types. We perform experiments on standard and widely used document collections including Clueweb09-B and Robust04 to evaluate our proposed indexing strategy from both effectiveness and efficiency perspectives. Based on our experiments, we find that when neural embeddings are used to build inverted indices; hence relaxing the requirement to explicitly observe the posting list key in the indexed document: (a) retrieval efficiency will increase compared to a standard inverted index, hence reduces the index size and query processing time, and (b) while retrieval efficiency, which is the main objective of an efficient indexing mechanism improves using our proposed method, retrieval effectiveness also retains competitive performance compared to the baseline in terms of retrieving a reasonable number of relevant documents from the indexed corpus. 相似文献
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搜索引擎的功能及其局限性探讨 总被引:11,自引:0,他引:11
搜索引擎具有网上信息收集、组织和检索三大功能,由于受人为因素和技术因素的影响,目前搜索引擎存在信息标引深度不够、信息占有量不足,查准率低,查全率不高,技术发展不完善,检索功能不全、索引数据库更新困难,挤占网络带宽、分工协作不强等局限性。 相似文献