高速铁路查询应答器查询天线的设计

阐述了高速铁路查询应答器系统工作原理,提出了查询天线的设计指标和设计过程,包括天线的分析、加载电阻、电容调节和匹配电路的优化仿真。通过不断的软件仿真和实验调整测试,采用一种新型的匹配电路结构实现了天线的双频工作。在设计频率范围内满足驻波要求,同时和应答器天线之间的传输性能好,可以应用于查询应答器系统。     

基于遗传迭代优化的云计算下海量数据分类查询

由于传统的数据分类查询方法查询失效所承担的额外操作代价不同,无法有效实现优化分类查询,提出一种基于变异遗传散布的云计算环境下海量数据优化分类查询方法,对于含有云计算环境下海量数据的数据库,给出遗传迭代查询散布及数据间的查询响应函数,获取云计算环境下海量数据灰度散布值,融入遗传执行算子数据,获取遗传变异散布分类查询系数,在充分宽的尺度和平移区域中获取最大类别之间的匹配值,得到数据查询响应函数,将其转换成变异遗传散布控制量,从而实现云计算环境下海量数据的优化分类查询。仿真实验结果表明,所提方法具有很高的精度。     

面向三类查询意图歧义性的查询表达式自动识别研究

【 目的/意义】针对查询意图歧义性自动识别,探讨特征有效性及采用不同分类算法识别三类查询意图歧义 性的分类准确率,以期对后续研究提供借鉴与指导。【方法/过程】首先提出了一个面向查询意图歧义性的查询表达 式分类体系;随后,构建了查询表达式特征及相关文档特征共六类;最后,分别采用决策树算法、神经网络算法及k 最邻近算法,探讨采用不同特征组合的有效性及不同分类算法的分类准确率。【结果/结论】①分类准确率较基准实 验提升比例为49.5%;②使用查询表达式特征分类优于使用相关文档特征;③决策树的分类准确率略高于其他两类 分类算法。【创新/局限】构建了一个面向查询意图歧义性的查询分类体系;完成了面向三类查询意图歧义性的分类 任务;然限于数据集获取途径,仅对200数据验证。     

汉字查询中两个问题的处理

用户在搜索引擎中进行汉语词句查询时,由于用户自己不很清楚某些词句应使用的确切的汉字或者不能准确的选择合适的汉语词汇,往往会造成查询不到用户所希望的结果或者结果范围超过了用户实际想要的范围。此时用户会希望系统能采取有效的措施给出用户一些可选择的选项以快速地定位至用户所要查询的内容上或缩减查询的结果。对于这种用户需求,可以采用汉语词句的模糊查询方法和查询结果的先行细化分类的方法以减少用户的重试输入的次数。     

浅谈数据库的模糊查询与优化查询

数据库查询是数据库的核心操作,本文详细介绍数据库系统中模糊查询技术和数据库查询优化技术的方法与技巧,提供了能实现真正模糊查询的二个通用函数的源程序,查询语言SELECT-SQL通配符的使用方法,以及查询优化中几种方法和技巧。     

基于多样约束模型的远程教育数据库优化查询算法

提出了一种基于多样约束模型的数据库优化查询方法。将数据库中数据进行分类处理,按照数据特征将数据库中信息分为多个不同的类别,为数据库优化查询提供准确的数据基础。建立多样约束模型,判断符合查询条件的数据类别,根据不同的数据类别进行数据查询,从而实现数据库优化查询。实验结果表明,利用这种算法进行数据库查询优化,能够有效提高数据库信息查询的效率。     

常识知识查询

给出了在一个面向Agent、本体和关系的常识知识库中如何实现常识知识查询的过程。在查询过程中,用自然语言形式描述的查询先被转化成一种我们所定义的面向Agent和本体的描述性查询语言。接着,描述性查询被翻译成SQL查询语句。用SQL查询语句对已转化成为关系形式的常识知识库进行查询后,自然语言生成算法把以关系表为形式的查询结果转化成自然语言。实验表明,新的查询方法在查询时间上优于原有的直接在agent和本体上查询知识的旧方法。实验数据表明,查询时间缩短了大约80 %。     

变异遗传散布控制执行算子的数据库分类查询

为提高数据查询的寻优能力和查询准确度,对数据库查询进行优化改进。提出一种新型的变异遗传散布控制执行算子数据库分类查询算法。设计遗传散布执行算子,实现对遗传算法数据库查询中的迭代控制,通过定义数据库查询散布值,通过对数据文本的最佳特征提取,通过散布分类查询系数的控制遗传迭代循环,对遗传算法的变异处理,实现最优数据信息相关匹配,可以很好地指导数据查询结果,实现数据库查询的准确性和有效性。仿真实验表明,新算法可以提高半连接下分布式数据库查询的效率,特征呈现高聚敛性匹配,数据查询特征相关匹配度达到97.3%,比传统算法提高了25.6%,寻优时间较传统算法大幅缩短。算法有效提高了数据库查询准确率和效率,具有较强的应用价值。     

嵌入式设备多存储介质媒体库统一查询方法

智能手机、带存储介质的嵌入式设备,一般会存储大量的图片、音乐、视频等多媒体文件。只有少部分媒体播放器带有文件的搜索和管理功能,但是功能有限,操作也不统一。首先对磁盘文件目录作递归扫描,根据文件名后缀作过滤和分类,用嵌入式数据库sqlite3来存储扫描结果;然后介绍了分类查询、文件名汉字拼音首字母查询、即输即查的快速模糊查询技术,还特别对多分区和多介质的情况作了处理,支持多介质的统一查询。此方法可用来轻易构建软件,给智能手机或其它嵌入式设备用户一种全方位的媒体库管理体验。     

基于数据库分类的deep web爬行器研究

在现有相关研究的基础上,设计一种基于数据库分类的deep web爬行器。该爬行器首先从抓取的网页中识别出deep web数据库的入口表单,然后采用查询探测方法对数据库进行自动分类,并根据分类结果来选取一组合适的关键词作为查询词,自动填写入口表单中的文本框并向数据库提出查询请求。实验结果表明,基于数据库分类的deep web爬行器的爬行效果要优于基于指定查询词的deep web爬行器的爬行效果。     

基于词嵌入语义的精准检索式构建方法

[目的/意义]使用科技文献数据库进行文献检索时,检索式中的关键词如果不够全面,将导致检索结果查全率较低;检索式中的关键词如果一词多义,则可能向检索结果中引入无关文献,导致查准率较低。[方法/过程]针对这两类问题,本文提出使用词嵌入这一新颖的文本数据化表现形式,一方面通过语义分析对检索关键词进行扩充从而提高查全率;另一方面通过发现语义异常点来提高查准率。[结果/结论]本文将该方法应用于人工智能领域中深度学习方向上的文献检索式构建,实验结果表明该方法能在一定程度上提高检索的查全率和查准率。     

非一致数据库的查询研究

提出的方法能灵活地进行数据库SQL查询,它可以干扰一系列的约束条件,并且这种方法能够允许用户假定一组约束和查询一起。系统重写这个查询去查找相应的与约束一致的数据。这个重写是SQL,以便能被商业数据库系统有效地优化和执行。使用TPC-H基准的数据和查询比较脏数据多粒度的执行性能,实验显示该方法是可行的。     

Detecting verbose queries and improving information retrieval

Although most of the queries submitted to search engines are composed of a few keywords and have a length that ranges from three to six words, more than 15% of the total volume of the queries are verbose, introduce ambiguity and cause topic drifts. We consider verbosity a different property of queries from length since a verbose query is not necessarily long, it might be succinct and a short query might be verbose. This paper proposes a methodology to automatically detect verbose queries and conditionally modify queries. The methodology proposed in this paper exploits state-of-the-art classification algorithms, combines concepts from a large linguistic database and uses a topic gisting algorithm we designed for verbose query modification purposes. Our experimental results have been obtained using the TREC Robust track collection, thirty topics classified by difficulty degree, four queries per topic classified by verbosity and length, and human assessment of query verbosity. Our results suggest that the methodology for query modification conditioned to query verbosity detection and topic gisting is significantly effective and that query modification should be refined when topic difficulty and query verbosity are considered since these two properties interact and query verbosity is not straightforwardly related to query length.     

Determining the informational,navigational, and transactional intent of Web queries

In this paper, we define and present a comprehensive classification of user intent for Web searching. The classification consists of three hierarchical levels of informational, navigational, and transactional intent. After deriving attributes of each, we then developed a software application that automatically classified queries using a Web search engine log of over a million and a half queries submitted by several hundred thousand users. Our findings show that more than 80% of Web queries are informational in nature, with about 10% each being navigational and transactional. In order to validate the accuracy of our algorithm, we manually coded 400 queries and compared the results from this manual classification to the results determined by the automated method. This comparison showed that the automatic classification has an accuracy of 74%. Of the remaining 25% of the queries, the user intent is vague or multi-faceted, pointing to the need for probabilistic classification. We discuss how search engines can use knowledge of user intent to provide more targeted and relevant results in Web searching.     

Adapting information retrieval systems to user queries

Users enter queries that are short as well as long. The aim of this work is to evaluate techniques that can enable information retrieval (IR) systems to automatically adapt to perform better on such queries. By adaptation we refer to (1) modifications to the queries via user interaction, and (2) detecting that the original query is not a good candidate for modification. We show that the former has the potential to improve mean average precision (MAP) of long and short queries by 40% and 30% respectively, and that simple user interaction can help towards this goal. We observed that after inspecting the options presented to them, users frequently did not select any. We present techniques in this paper to determine beforehand the utility of user interaction to avoid this waste of time and effort. We show that our techniques can provide IR systems with the ability to detect and avoid interaction for unpromising queries without a significant drop in overall performance.     

On the use of negation in Boolean IR queries

The negation operator, in various forms in which it appears in Information Retrieval queries, is investigated. The applications include negated terms in Boolean queries, more specifically in the presence of metrical constraints, but also negated characters used in the definition of extended keywords by means of regular expressions. Exact definitions are suggested and their usefulness is shown on several examples. Finally, some implementation issues are discussed, in particular as to the order in which the terms of long queries, with or without negated keywords, should be processed, and efficient heuristics for choosing a good order are suggested.     

Transitive dictionary translation challenges direct dictionary translation in CLIR

The paper reports on experiments carried out in transitive translation, a branch of cross-language information retrieval (CLIR). By transitive translation we mean translation of search queries into the language of the document collection through an intermediate (or pivot) language. In our experiments, queries constructed from CLEF 2000 and 2001 Swedish, Finnish and German topics were translated into English through Finnish and Swedish by an automated translation process using morphological analyzers, stopword lists, electronic dictionaries, n-gramming of untranslatable words, and structured and unstructured queries. The results of the transitive runs were compared to the results of the bilingual runs, i.e. runs translating the same queries directly into English. The transitive runs using structured target queries performed well. The differences ranged from −6.6% to +2.9% units (or −25.5% to +7.8%) between the approaches. Thus transitive translation challenges direct translation and considerably simplifies global CLIR efforts.     

GTE-Rank: A time-aware search engine to answer time-sensitive queries

In the web environment, most of the queries issued by users are implicit by nature. Inferring the different temporal intents of this type of query enhances the overall temporal part of the web search results. Previous works tackling this problem usually focused on news queries, where the retrieval of the most recent results related to the query are usually sufficient to meet the user's information needs. However, few works have studied the importance of time in queries such as “Philip Seymour Hoffman” where the results may require no recency at all. In this work, we focus on this type of queries named “time-sensitive queries” where the results are preferably from a diversified time span, not necessarily the most recent one. Unlike related work, we follow a content-based approach to identify the most important time periods of the query and integrate time into a re-ranking model to boost the retrieval of documents whose contents match the query time period. For that purpose, we define a linear combination of topical and temporal scores, which reflects the relevance of any web document both in the topical and temporal dimensions, thus contributing to improve the effectiveness of the ranked results across different types of queries. Our approach relies on a novel temporal similarity measure that is capable of determining the most important dates for a query, while filtering out the non-relevant ones. Through extensive experimental evaluation over web corpora, we show that our model offers promising results compared to baseline approaches. As a result of our investigation, we publicly provide a set of web services and a web search interface so that the system can be graphically explored by the research community.     

New query suggestion framework and algorithms: A case study for an educational search engine

Query suggestion is generally an integrated part of web search engines. In this study, we first redefine and reduce the query suggestion problem as “comparison of queries”. We then propose a general modular framework for query suggestion algorithm development. We also develop new query suggestion algorithms which are used in our proposed framework, exploiting query, session and user features. As a case study, we use query logs of a real educational search engine that targets K-12 students in Turkey. We also exploit educational features (course, grade) in our query suggestion algorithms. We test our framework and algorithms over a set of queries by an experiment and demonstrate a 66–90% statistically significant increase in relevance of query suggestions compared to a baseline method.     

Real time search on the web: Queries,topics, and economic value

Real time search is an increasingly important area of information seeking on the Web. In this research, we analyze 1,005,296 user interactions with a real time search engine over a 190 day period. Using query log analysis, we investigate searching behavior, categorize search topics, and measure the economic value of this real time search stream. We examine aggregate usage of the search engine, including number of users, queries, and terms. We then classify queries into subject categories using the Google Directory topical hierarchy. We next estimate the economic value of the real time search traffic using the Google AdWords keyword advertising platform. Results shows that 30% of the queries were unique (used only once in the entire dataset), which is low compared to traditional Web searching. Also, 60% of the search traffic comes from the search engine’s application program interface, indicating that real time search is heavily leveraged by other applications. There are many repeated queries over time via these application program interfaces, perhaps indicating both long term interest in a topic and the polling nature of real time queries. Concerning search topics, the most used terms dealt with technology, entertainment, and politics, reflecting both the temporal nature of the queries and, perhaps, an early adopter user-based. However, 36% of the queries indicate some geographical affinity, pointing to a location-based aspect to real time search. In terms of economic value, we calculate this real time search stream to be worth approximately US $33,000,000 (US $33 M) on the online advertising market at the time of the study. We discuss the implications for search engines and content providers as real time content increasingly enters the main stream as an information source.