一种面向用户兴趣的个性化语义查询扩展方法

在基于本体的语义查询扩展研究的基础上,结合用户模型的研究,提出要将用户的兴趣模型与查询扩展相结合,实现个性化的语义查询扩展,并把个性化的语义查询扩展过程分为两个阶段——检索关键词向用户模型中的个性化领域本体概念的映射以及在本体层次对映射概念的语义扩展,给出每一阶段的实现算法。实验表明该方法能够提高信息检索的查准率和查全率,在一定程度上满足个性化的查询需求。     

图书馆OPAC与图书搜索引擎、网上书店的功能比较和启示

OPAC即联机公共书目查询系统,是利用计算机终端来查询馆藏数据资源的一种现代化检索系统。OPAC与图书搜索引擎、网上书店三者具有相同的基本功能：查询、揭示图书,但图书搜索引擎、网上书店以其检索界面简洁友好、检索功能强大、书目信息丰富、排序科学化、用户互动性好,冲击威胁着图书馆OPAC。因此,图书馆OPAC应借鉴图书搜索引擎和网上书店的做法,改进OPAC功能、增加书目记录的可见性、建立各种独立于图书馆集成系统的新型OPAC。     

基于Access的信息管理系统开发实践——查询程序设计与数据库系统功能实现

在各类信息管理系统研发中。通过设计Access数据库的选择查询、交叉表查询、参数查询、操作查询等查询功能。充分利用Access内嵌了SQL—SELECT查询语句中的联合查询、传递查询和数据定义等特有查询设计。息的统计分析。以满足管理人员和用户对数据进行录入、维护和科研信息的检索、应用。实现数据处理控制、动态数据统计分析、数值自动计算、数据剔选查询等系统功能。     

基于数据集成的高校图书馆个性化信息服务研究

本文论述了高校图书馆开展个性化信息服务的现状,Deep web数据集成的加入可以使得个性化信息服务更有优势,分析了Deep web的整合方法和数据集成系统框架,并在此基础上设计了高校图书馆个性化信息服务的系统.     

利用查询重构识别查询意图

基于AOL查询日志数据集,在不给定查询意图类目体系情况下,尝试利用查询重构来识别用户查询意图。主要探讨如何识别出能表达原查询用户意图的查询重构以及如何对识别的查询意图进行聚类两个问题。人工评测结果表明,该方法能够取得较好的实验效果。     

基于搜索引擎分类信息的用户查询歧义消减

用户在利用搜索引擎进行信息检索时,查询条件往往存在歧义,这导致搜索结果的多样性和冗余性.传统的方法主要是基于语义分析或构建知识库,此类方法在实际应用中的可行性不高.本文基于搜索引擎的分类信息,实现了一个简单有效的分类搜索系统.它首先根据用户的查询条件,将返回的搜索结果进行分类,并以树形目录的形式展示给用户,而后根据用户的点击数据,逐步确定用户的搜索意图,从而达到了查询歧义消减的目的.论文详细介绍了系统的设计思想、架构和工作流程.测试实例表明,该系统可以在一定程度上确定用户的查询意图,为用户返回更加准确的搜索结果.     

A New Method of Detecting Query Intent for Search Engines

查询背后的信息需求被定义为查询意图,搜索引擎可以根据不同的查询意图,提供多样性的服务,优化检索效果.查询意图的识别多被看成是一种分类问题,现有的大多数方法都基于查询串文本本身的特征和查询的用户点击数据特征.这两种方法存在如下的困难:对于查询串的文本特征,查询比较短,特征比较稀疏,要进行比较准确的理解会比较困难;对于用户点击特征,由于用户提交查询的长尾性分布,大多数查询的提交次数都是较少的,对于这些查询,要判别它们的意图是比较困难的.为了克服长尾查询上查询意图判断的不可靠问题,本文提出利用查询结果的相关性分数的分布作为特征来判断查询意图的方法.这种方法依赖查询结果的特征,比查询串本身的特征更加丰富;同时不依赖于用户的点击数据,因此可以克服长尾查询上的困难.结果表明,使用结果分数分布,可以提高意图判别的准确程度.     

Objective Measurement of Related Queries Based on Semantics and Truth Scale

相关查询可以给用户推荐合适的查询以辅助用户快速获得需要的信息,其中根据原始查询选择相关查询的优劣是相关查询有效性的关键.提出利用用户查询日志获得查询语义相关性,其关键是定义了三个与原始查询相关的相似性测度:查询串流行程度、查询串字面相似性和查询串之间语义相似性.使用真值程度度量,将查询串的三个测度度量值映射成一个查询相关性客观评价值,在计算过程中还能得到每个特性的优劣程度.实验结果表明:语义相关查询及其客观评价方法可以显著提高相关查询质量,主、客观评价值之间具有相当高的相关性,表明了本方法挖掘相关查询和评价相关查询的有效性.     

数字图书馆包装层生成技术研究

包装层是对应于特定信息源的一种特殊程序,是实现异构信息源集成的关键。数字图书馆的包装层可以作为数字图书馆的智能前端代理,负责把来自中介层的统一查询表示映射为针对具体数字图书馆的查询格式,并从得到的查询结果中提取出用户需要的信息,提交给中介层。数字图书馆包装层生成的关键技术主要有:查询映射机制,查询服务调用机制,结果提取与转换机制。图6。参考文献5。     

广电BOSS系统之网上营业厅系统的建设

系统概述 网上营业厅可以为所有客户提供业务办理、综合查询、客户服务等标准服务,为大客户提供VIP服务等差异化服务。系统的主要功能有：实现用户消费账单数据的查询展现,用户可以足不出户,方便快捷地通过各种手段了解到个人消费情况;提供统一的服务受理接口,提供缴费和业务办理功能,如套餐办理、新产品办理等,同时可实现用户的故障申告、投诉、建议等功能,并能通过网上营业厅以邮件及短信等方式反馈给用户;提供相应的服务查询接口,可实现用户积分查询、套餐状态查询、业务状态查询、营业厅导航等功能。     

基于WordNet和SUMO本体集成的自动语义检索及可视化模型

针对语义检索在实际应用中面临的用户查询意图获取困难、潜在语义索引计算复杂、领域本体覆盖范围小、概念语义类型不丰富、自动化程度低等问题,提出基于WordNet和SUMO本体集成的自动语义检索及可视化模型。实验表明这种模型能够过滤掉大量与用户查询无关的信息,提高信息检索系统的检准率,并很好地满足用户可视化和个性化检索需求。     

基于OPAC日志的高校图书馆用户提问调整模式研究——以北京师范大学图书馆为例

[目的/意义] 探讨高校图书馆用户在使用图书馆OPAC系统查找相关资源时调整提问的行为模式。[方法/过程] 以北京师范大学图书馆OPAC日志数据为对象,采用S.Y.Rieh与Xie Hong提出的提问调整模式类型,利用内容分析法对提问日志进行内容编码和统计分析。[结果/结论] 高校图书馆用户的OPAC提问调整基本模式与网络信息检索提问调整模式基本一致,并且,在动态调整模式过程中,还可以细化为直线、阶梯、锯齿、凹凸、循环等子模式。针对如何优化OPAC系统和提升用户信息素养提出若干建议。     

Webometric research with the Bing Search API 2.0

In May 2011 the Bing Search API 2.0 had become the only major international web search engine data source available for automatic offline processing for webometric research. This article describes its key features, contrasting them with previous web search data sources, and discussing implications for webometric research. Overall, it seems that large-scale quantitative web research is possible with the Bing Search API 2.0, including query splitting, but that legal issues require the redesign of webometric software to ensure that all results obtained from Bing are displayed directly to the user.     

Improving search via personalized query expansion using social media

Social tagging systems have gained increasing popularity as a method of annotating and categorizing a wide range of different web resources. Web search that utilizes social tagging data suffers from an extreme example of the vocabulary mismatch problem encountered in traditional information retrieval (IR). This is due to the personalized, unrestricted vocabulary that users choose to describe and tag each resource. Previous research has proposed the utilization of query expansion to deal with search in this rather complicated space. However, non-personalized approaches based on relevance feedback and personalized approaches based on co-occurrence statistics only showed limited improvements. This paper proposes a novel query expansion framework based on individual user profiles mined from the annotations and resources the user has marked. The underlying theory is to regularize the smoothness of word associations over a connected graph using a regularizer function on terms extracted from top-ranked documents. The intuition behind the model is the prior assumption of term consistency: the most appropriate expansion terms for a query are likely to be associated with, and influenced by terms extracted from the documents ranked highly for the initial query. The framework also simultaneously incorporates annotations and web documents through a Tag-Topic model in a latent graph. The experimental results suggest that the proposed personalized query expansion method can produce better results than both the classical non-personalized search approach and other personalized query expansion methods. Hence, the proposed approach significantly benefits personalized web search by leveraging users’ social media data.     

一种新的用户需求组织方式：需求语义网络

提出基于关联数据技术组织用户需求的设想及其架构——需求语义网络模型,该模型由数据层、需求信息层、应用层组成,需求信息层是整个模型的核心,其构建包括需求信息建模、需求信息命名、需求信息RDF化、需求信息发布、开放查询5个步骤,需求语义网络构建的重点和难点包括用户需求及关系的定义与描述、用户需求的关联与分解、需求网络中各层次之间的协作与交流以及匹配服务器的延伸和扩展等,最后,将需求语义网络理论应用到高校图书馆个性化知识服务中,提出基于关联数据的高校图书馆图书需求语义网络的构建模型。     

Identifying top relevant dates for implicit time sensitive queries

Despite a clear improvement of search and retrieval temporal applications, current search engines are still mostly unaware of the temporal dimension. Indeed, in most cases, systems are limited to offering the user the chance to restrict the search to a particular time period or to simply rely on an explicitly specified time span. If the user is not explicit in his/her search intents (e.g., “philip seymour hoffman”) search engines may likely fail to present an overall historic perspective of the topic. In most such cases, they are limited to retrieving the most recent results. One possible solution to this shortcoming is to understand the different time periods of the query. In this context, most state-of-the-art methodologies consider any occurrence of temporal expressions in web documents and other web data as equally relevant to an implicit time sensitive query. To approach this problem in a more adequate manner, we propose in this paper the detection of relevant temporal expressions to the query. Unlike previous metadata and query log-based approaches, we show how to achieve this goal based on information extracted from document content. However, instead of simply focusing on the detection of the most obvious date we are also interested in retrieving the set of dates that are relevant to the query. Towards this goal, we define a general similarity measure that makes use of co-occurrences of words and years based on corpus statistics and a classification methodology that is able to identify the set of top relevant dates for a given implicit time sensitive query, while filtering out the non-relevant ones. Through extensive experimental evaluation, we mean to demonstrate that our approach offers promising results in the field of temporal information retrieval (T-IR), as demonstrated by the experiments conducted over several baselines on web corpora collections.     

Exploiting entity relationship for query expansion in enterprise search

Enterprise search is important, and the search quality has a direct impact on the productivity of an enterprise. Enterprise data contain both structured and unstructured information. Since these two types of information are complementary and the structured information such as relational databases is designed based on ER (entity-relationship) models, there is a rich body of information about entities in enterprise data. As a result, many information needs of enterprise search center around entities. For example, a user may formulate a query describing a problem that she encounters with an entity, e.g., the web browser, and want to retrieve relevant documents to solve the problem. Intuitively, information related to the entities mentioned in the query, such as related entities and their relations, would be useful to reformulate the query and improve the retrieval performance. However, most existing studies on query expansion are term-centric. In this paper, we propose a novel entity-centric query expansion framework for enterprise search. Specifically, given a query containing entities, we first utilize both unstructured and structured information to find entities that are related to the ones in the query. We then discuss how to adapt existing feedback methods to use the related entities and their relations to improve search quality. Experimental results over two real-world enterprise collections show that the proposed entity-centric query expansion strategies are more effective and robust to improve the search performance than the state-of-the-art pseudo feedback methods for long natural language-like queries with entities. Moreover, results over a TREC ad hoc retrieval collections show that the proposed methods can also work well for short keyword queries in the general search domain.     

Click-based evidence for decaying weight distributions in search effectiveness metrics

Search effectiveness metrics are used to evaluate the quality of the answer lists returned by search services, usually based on a set of relevance judgments. One plausible way of calculating an effectiveness score for a system run is to compute the inner-product of the run’s relevance vector and a “utility” vector, where the ith element in the utility vector represents the relative benefit obtained by the user of the system if they encounter a relevant document at depth i in the ranking. This paper uses such a framework to examine the user behavior patterns—and hence utility weightings—that can be inferred from a web query log. We describe a process for extrapolating user observations from query log clickthroughs, and employ this user model to measure the quality of effectiveness weighting distributions. Our results show that for measures with static distributions (that is, utility weighting schemes for which the weight vector is independent of the relevance vector), the geometric weighting model employed in the rank-biased precision effectiveness metric offers the closest fit to the user observation model. In addition, using past TREC data as to indicate likelihood of relevance, we also show that the distributions employed in the BPref and MRR metrics are the best fit out of the measures for which static distributions do not exist.     

基于领域特征和用户查询取样的Deep Web数据源描述方法

[目的/意义]数据源描述（又称数据源摘要）是Deep Web集成检索领域存在的关键问题之一,数据源描述的质量直接影响着集成检索系统的检索效率和效果。本文提出一种基于领域特征和用户查询取样的数据源描述方法,以期为非合作环境下资源集成应用与研究提供参考和借鉴。[方法/过程]该方法为异构非合作型数据源的离线取样方法,通过分析数据源和用于查询的领域主题属性,依次构建领域特征词集、初始特征词集和高频特征词集,并最终获得以高频特征词查询取样的数据源描述信息。结合流行的CORI算法,深入分析基于推理网络的用户查询与数据源描述的相关度计算方法,并基于此方法设计基于Lemur工具集的集成检索系统,验证了上述方法的有效性。[结果/结论]所提方法在查全率和查准率方面均得到很好的表现。与其他方法相比,该方法在样本数据自动更新和运维管理方面具有明显成本优势和实用价值。