基于元数据与Z39.50的分布协作式Web信息检索

Web上大量的异质、分布、动态的信息造成了“信息过载”.如何有效地为用户提供Web信息检索已经成为一项重要的研究课题.Web搜索引擎部分地解决了信息检索问题,然而其效果却远远不能令人满意.提出了Web信息检索的分布协作策略以取代传统的集中式信息检索方式;给出了一种新的Web信息检索系统模型,该模型支持对Web文档的元数据进行检索,并采用Z39.50协议作为接口标准,以克服不同信息检索系统之间的访问异构性.在此基础上,设计了一个分布协作式Web信息检索框架,用以帮助用户有效地进行Web信息检索.     

Information retrieval from the World Wide Web: a user-focused approach based on individual experience with search engines

Although search engines are essential tools for finding information on the World Wide Web, the effective use of search engines for information retrieval (IR) is a crucial challenge for any Internet user. Based on the user-focused approach, this study investigates individual information retrieval behaviors using information processing theory. The results show that experience with search engines significantly affects users’ attitudes toward search engines for information retrieval, the query-based service is more popular than the directory-based service, users are not completely satisfied with the precision of retrieved information and the response time of search engines, and users’ motivation is a key factor that predicts their intention to use search engines for information retrieval. Furthermore, this study proposes a conceptual model for investigating individual attitudes toward search engines for information retrieval.     

Web搜索引擎框架研究

Ｗｅｂ搜索引擎是Ｉｎｔｅｒｎｅｔ上非常有用的信息检索工具,但是由于目前搜索引擎检索出的信息量庞大,且一个特定的搜索引擎主要包含某一特定领域的信息,这使得用户很难从某一个搜索引擎获得准确的导航信息。文中提出一个新的Ｗｅｂ搜索引擎框架ＧＳＥ,并提出了一个适合于Ｗｅｂ信息获取与处理的语言ＷＥＲＰＬ。通过ＷＩＲＰＬ可以将多个Ｗｅｂ搜索引擎结合起来,为用户提供一个一致、高效、准确的Ｗｅｂ搜索引擎。     

基于中文搜索引擎网络信息用户行为研究*

为了更好地理解中文搜索用户的检索行为,首先建立一个搜索引擎选择平台,主要是用来生成研究中所需的日志文件;然后从中英文用户的搜索行为差异的角度出发,对日志文件进行深入研究,包括各中文搜索引擎使用率比较以及中文用户输入查询行为的一些规律等。研究结果表明,对准确地评测搜索引擎检索的效果以及未来中文搜索引擎设计的改进都有较好的指导意义。     

Visualizing Web search results in 3D

The exponential growth in Web sites is making it increasingly difficult to extract useful information on the Internet using existing search engines. Despite a wide range of sophisticated indexing and data retrieval features, search engines often deliver satisfactory results only when users know precisely what they are looking for. Traditional textual interfaces present results as a list of links to Web pages. Because most users are unwilling to explore an extensive list, search engines arbitrarily reduce the number of links returned, aiming also to provide quick response times. Moreover, their proprietary ranking algorithms often do not reflect individual user preferences. Those who need comprehensive general information about a topic or have vague initial requirements instead want a holistic presentation of data related to their queries. To address this need, we have developed Periscope, a 3D search result visualization system that displays all the Web pages found in a synthetic, yet comprehensible format.     

Mining the Web's link structure

The Web is a hypertext body of approximately 300 million pages that continues to grow at roughly a million pages per day. Page variation is more prodigious than the data's raw scale: taken as a whole, the set of Web pages lacks a unifying structure and shows far more authoring style and content variation than that seen in traditional text document collections. This level of complexity makes an “off-the-shelf” database management and information retrieval solution impossible. To date, index based search engines for the Web have been the primary tool by which users search for information. Such engines can build giant indices that let you quickly retrieve the set of all Web pages containing a given word or string. Experienced users can make effective use of such engines for tasks that can be solved by searching for tightly constrained key words and phrases. These search engines are, however, unsuited for a wide range of equally important tasks. In particular, a topic of any breadth will typically contain several thousand or million relevant Web pages. How then, from this sea of pages, should a search engine select the correct ones-those of most value to the user? Clever is a search engine that analyzes hyperlinks to uncover two types of pages: authorities, which provide the best source of information on a given topic; and hubs, which provide collections of links to authorities. We outline the thinking that went into Clever's design, report briefly on a study that compared Clever's performance to that of Yahoo and AltaVista, and examine how our system is being extended and updated     

一种基于概念的交互式Web搜索模型

当前基于关键字查询的大多数搜索引擎都没有提供个性化的用户服务，搜索结果主要根据关键字与文档的相似度来排序，这很难满足用户对日益膨胀的信息资源的需求。面对用户越来越难以迅速精确地检索到所需信息的现状，本文提出一种应用于LAN中的基于概念的三层搜索引擎模型：通过用户交互的方式，使得搜索具有个性化、智能化的特点。     

书籍搜索领域Deep Web数据集成系统

随着在线数据库的迅速增长,可以访问的数据库资源大大增多,但它们的信息传统搜索引擎无法获得,它隐藏在网站背后,成为人们快速有效获取信息的障碍。为了获得Deepweb中大量有价值的隐藏信息,需要整合各在线异构数据源,以便在同一领域内比较某一事物的大量相关信息。目前,越来越多的人采取网上买书的消费方式,针对这个消费热点问题,设计了一个书籍搜索领域的Deep Web数据集成系统,提供一个集成的查询接口,使得用户可以方便地进行查找和比对。     

Web信息检索研究进展

Ｗeb上大量、分布、动态的信息造成了“信息过载”,如何在传统信息检索技术的基础上开展针对Ｗeb的检索工作已经成为一基项重要的研究课题,但是,繁多的Ｗeb信息检索系统和各种模糊的概念给用户的选择和研究人员的讨论带来了不便。同时,有关Ｗeb信息检索最新技术的比较完整的分析又十分缺乏。在此,对Ｗeb信息检索技术进行了综述,从Ｗeb信息检索系统的层次化分类（搜索引擎与目录、元搜索引擎、信息检索agent)、一般机制和关键新技术（基于超链的相关度排序、检索结果的联机聚类、基于概念的检索、相关度反馈）等方面加以阐述,以期对感兴趣的同行有参考作用。     

Exploiting Query Repetition and Regularity in an Adaptive Community-Based Web Search Engine

Search engines continue to struggle with the challenges presented by Web search: vague queries, impatient users and an enormous and rapidly expanding collection of unmoderated, heterogeneous documents all make for an extremely hostile search environment. In this paper we argue that conventional approaches to Web search -- those that adopt a traditional, document-centric, information retrieval perspective -- are limited by their refusal to consider the past search behaviour of users during future search sessions. In particular, we argue that in many circumstances the search behaviour of users is repetitive and regular; the same sort of queries tend to recur and the same type of results are often selected. We describe how this observation can lead to a novel approach to a more adaptive form of search, one that leverages past search behaviours as a means to re-rank future search results in a way that recognises the implicit preferences of communities of searchers. We describe and evaluate the I-SPY search engine, which implements this approach to collaborative, community-based search. We show that it offers potential improvements in search performance, especially in certain situations where communities of searchers share similar information needs and use similar queries to express these needs. We also show that I-SPY benefits from important advantages when it comes to user privacy. In short, we argue that I-SPY strikes a useful balance between search personalization and user privacy, by offering a unique form of anonymous personalization, and in doing so may very well provide privacy-conscious Web users with an acceptable approach to personalized search.     

基于语义的Web信息检索

用户要从网络中得到所需的信息一般是通过各种搜索引擎。但是现有的搜索引擎都存在着检索相关度不高等问题。随着语义Web概念的提出及相关技术的发展,基于语义的Web信息检索逐渐成为了语义Web研究的热点。给出了传统搜索引擎存在的问题,从理论上分析了如何将语义Web技术融入Web信息检索中去,并在理论分析的基础上给出了基于语义的Web信息检索的模型。     

Optimising Publications for Google Users

Abstract

Librarians should think explicitly about Google users whenever they publish on the Web, and should update their policies and procedures accordingly. The article describes procedures that libraries can adopt to ensure that their publications are optimised for access by users of Google and other Web search engines. The aim of these procedures is to enhance resource discovery and information retrieval, and to enhance the reputation of libraries as valued custodians of published information, as well as exemplars of good practice in information management.     

基于搜索结果的个性化推荐系统研究

搜索引擎返回的信息太多且不能根据用户的兴趣提供检索结果,使得用户使用搜索引擎难以用简便的方式找到感兴趣的文档。个性化推荐是一种旨在减轻用户在信息检索方面负担的有效方法。文中把内容过滤技术和文档聚类技术相结合,实现了一个基于搜索结果的个性化推荐系统,以聚类的方法自动组织搜索结果,主动推荐用户感兴趣的文档。通过建立用户概率兴趣模型,对搜索结果STC聚类的基础上进行内容过滤。实验表明,概率模型比矢量空间模型更好地表达了用户的兴趣和变化。     

Personalized Web search for improving retrieval effectiveness

Current Web search engines are built to serve all users, independent of the special needs of any individual user. Personalization of Web search is to carry out retrieval for each user incorporating his/her interests. We propose a novel technique to learn user profiles from users' search histories. The user profiles are then used to improve retrieval effectiveness in Web search. A user profile and a general profile are learned from the user's search history and a category hierarchy, respectively. These two profiles are combined to map a user query into a set of categories which represent the user's search intention and serve as a context to disambiguate the words in the user's query. Web search is conducted based on both the user query and the set of categories. Several profile learning and category mapping algorithms and a fusion algorithm are provided and evaluated. Experimental results indicate that our technique to personalize Web search is both effective and efficient.     

Prototyping an integrated information gathering system on CORBA

The sheer volume of information and variety of sources from which it may be retrieved on the Web make searching the sources a difficult task. Usually, meta-search engines can be used only to search Web pages or documents; other major sources such as data bases, library corpuses and the so-called Web data bases are not involved. Faced with these restrictions, an effective retrieval technology for a much wider range of sources becomes increasingly important. In our previous work, we proposed an Integrated Retrieval (IIR), which is based on Common Object Request Broker Architecture, to spare clients the trouble of complicated semantics when federating multiple sources. In this paper, we present an IIR-based prototype for integrated information gathering system. It offers a unified interface for querying heterogeneous interfaces or protocols of sources and uses SQL compatible query language for heterogeneous backend targets. We use it to link two general search engines (Yahoo and AltaVista), a science paper explorer (IEEE), and two library corpus explorers. We also perform preliminary measurements to assess the potential of the system. The results shown that the overhead spent on each source as the system queries them is within reason, that is, that using IIR to construct an integrated gathering system incurs low overhead.     

Adaptive Web Search: Evolving a Program That Finds Information

Anyone who's used a computer to find information on the Web knows that the experience can be frustrating. Search engines are incorporating new techniques (such as examining document link structures) to increase effectiveness. However, searchers all too often face one of two outcomes: reviewing many more Web pages than they'd prefer or failing to find as much useful information as they really want. We introduce a new retrieval technique that exploits users' persistent information needs. These users might include business analysts specializing in genetic technologies, stockbrokers keeping abreast of wireless communications, and legislators needing to understand computer privacy and security developments. To help such searchers, we evolve effective search programs by using feedback based on users' judgments about the relevance of the documents they've retrieved. This approach uses genetic programming to automatically evolve new retrieval algorithms based on a user's evaluation of previously viewed documents     

基于网络用户行为的搜索引擎系统SISI

提出了一种基于网络用户行为的搜索引擎SISl(Similar Interest，Similar access on Internet)。SISI的查询输入是一个Web文档的URL。SISI的检索模型是使用统计的方法基于网络日志中用户对文档的访问频率挖掘相关文档，充分利用了用户在相关文档判定上的潜在意识。模型的假设基础是一组兴趣相似的人访问的文档有可能相关。与传统的搜索引擎相比较，搜索引擎SISI具有系统初始化时间代价小、空间代价小等优点。同时SISI的检索优势在于可以查找那些没有显式相似内容的相关文档，尤其是在检索处理时避开了文档的类型，将文本文档和多媒体文档一视同仁。     

一种新的协作式搜索引擎的设计与实现

该文提出了一种分布式信息检索系统,叫作协作式搜索引擎(CSE),它是由多个相互协作的本地元搜索引擎构成的。每一个本地搜索引擎都有它自己的索引数据库,能够很快地进行更新。CSE通过基于站点选择搜索和对Web文档计分等方法来减少通信延迟、缩短收集时间,实现快速收集、及时更新和定位准确,从而克服了目前的搜索引擎更新周期太长的缺点。     

Performing Binary-Categorization on Multiple-Record Web Documents Using Information Retrieval Models and Application Ontologies

To retrieve Web documents of interest, most of the Web users rely on Web search engines. All existing search engines provide query facility for users to search for the desired documents using search-engine keywords. However, when a search engine retrieves a long list of Web documents, the user might need to browse through each retrieved document in order to determine which document is of interest. We observe that there are two kinds of problems involved in the retrieval of Web documents: (1) an inappropriate selection of keywords specified by the user; and (2) poor precision in the retrieved Web documents. In solving these problems, we propose an automatic binary-categorization method that is applicable for recognizing multiple-record Web documents of interest, which appear often in advertisement Web pages. Our categorization method uses application ontologies and is based on two information retrieval models, the Vector Space Model (VSM) and the Clustering Model (CM). We analyze and cull Web documents to just those applicable to a particular application ontology. The culling analysis (i) uses CM to find a virtual centroid for the records in a Web document, (ii) computes a vector in a multi-dimensional space for this centroid, and (iii) compares the vector with the predefined ontology vector of the same multi-dimensional space using VSM, which we consider the magnitudes of the vectors, as well as the angle between them. Our experimental results show that we have achieved an average of 90% recall and 97% precision in recognizing Web documents belonged to the same category (i.e., domain of interest). Thus our categorization discards very few documents it should have kept and keeps very few it should have discarded.