关键字检索

基于安全访问控制的XML关键字检索

XML Keyword Search Based on Secure Access Control

关系数据库中的XML关键字检索技术

Keyword Search for XML in Relational Databases

ORACLE科技信息管理系统中实现关键字检索

The Implementation of keywords Index in ORACLE Scientific Technology MIS

信息时代，Internet已经成为人们获取信息的重要途径，而基于关键字检索的Internet搜索引擎又是人们从互联网上查找信息的主要方法。

Internet is one of the most important way of getting information in information times . Internet search engine based on keywords has become the main means of searching information on internet .

研究了关系数据库中XML文档的关键字检索技术，提出了两种新的倒排索引技术：扩展的基于包含关系的倒排索引和基于模式的倒排索引。

This paper studies the keyword search for XML in relational databases , presents two inverted list indexes : extended inverted index based on containment relationship and inverted index based on schema .

同时，综合考虑语义检索和关键字检索两种情况，提出一种相似度计算方法，有效地满足了用户对Web信息资源的要求，并为以后的语义检索研究奠定了理论基础。

Meanwhile , comprehensively considering both semantic and keyword-based search , a similarity measuring algorithm was proposed , so that the system could effectively meet the requirements of information searching of users and laid a theoretical basis for the research on the semantic search .

RDF图上的关键字检索近年来已成为RDF研究领域中的一个热点，它将RDF处理技术和信息检索结合起来，为用户提供更加友好的检索方式。

Keyword search over RDF , which combines RDF technique with information retrieval , and provides users a friendly searching way , has also been a hot research area .

实验表明本文所提出的上下文感知的RDF图上的关键字检索方法可以有效地改进查询结果及其排序质量。

Extensive experimental results show that our proposed context-aware approach for keyword search over RDF can effectively improve the quality of searching results and their ranking . Also , our approach is efficient enough .

以往的PIR方案大多不能实现关键字检索，而是通过物理地址检索，我们深入研究了支持关键字检索的PIR。

Most of the previous private information retrieval scheme is search through physical address instead of keyword search . We further research on private information retrieval which support keyword search .

基于向量空间模型的多关键字检索技术

The Technology of Multi-keyword Information Retrieval Based on VSM

随着互联网上数据的急剧增长，关键字检索的不足逐渐显现出来。

With the rapid increase of data on the Internet , shortages of keyword retrieval emerge gradually .

基于关键字检索技术速度快、实现简单，但人工标注费时费力，且图档内容往往不易用文字表述。

Keyword-based retrieval technology is fast and simple , whereas manual labeling takes a lot of troubles .

迄今为止，国内外已见诸报导的计算机辅助木材识别系统中所采用的识别方法均属于多关键字检索的应用范畴。

So far , most of the announced computer-aided wood identification systems have used the multi-keyword retrieval method .

对于现存的基于关键字检索的搜索引擎来说，本系统模型已初步实现了搜索引擎的智能化，在一定程度上提高了搜索引擎的查全率和查准率。

This system has realized initial intelligence of search engine , and improves the preciseness and recall of search engine to a degree .

提出两阶段服务发现算法，实现了模糊关键字检索、混合检索等多种复杂查询功能。

The two-stage service discovery algorithm is proposed to realize many functions for complex query ( such as fuzzy keyword search , mixed search and etc. ) .

利用课件开发工具集成课程内容，完成与网络课程学习直接有关的课程内容展示、课程设计、关键字检索等教学环境的设计及其技术实现。

The thesis achieved network courseware by using design tool and carried out teaching environment such as course content , course design , keyword search and technology reality .

本课题的主要目的是将本体技术应用到医疗领域本体的信息采集中，从而提高搜索引擎检索的质量，这种方式改变了传统的依赖关键字检索时的查全率和查准率不高的弊病。

In this way , it improves the quality of search engines , this approach has changed the traditional reliance on keyword search at the time of the recall and precision is not high .

但传统的信息检索技术主要是基于字符串匹配的关键字检索技术，对语义匹配的支持能力较差，信息检索的查全率和查准率难以令人满意。

However , traditional information retrieval techniques is mainly based on string matching keyword search technology , the ability to support poor semantic matching , information retrieval recall rate and precision rate far from satisfactory .

为了提高搜索的准确率，本文提出了基于双关键字检索模型的海明码嵌入技术与基于距离权重的软量化技术，并通过实验证明这些技术有效增强了双关键字图像检索模型。

To improve the search accuracy , hamming embedding based on visual word pairs and distance-based soft quantization are introduced into the visual pair model , and the experiment results also verified the effectiveness of these technologies .

而且对于关键字检索方式的读取，你需要基于文件系统，而不是关系数据库管理系统（在没有表连接、约束或触发器的前提下，关系数据库管理系统就是一个索引了的文件系统）。

And you need keyed lookup for reads built on top of the FS , not in the RDBMS ( on the basis that an RDBMS with no joins , constraints or triggers is an indexed filesystem ) .

但是非结构化Web与基于关键字匹配检索的固有缺陷限制了分布式异构信息资源的共享与机器自动处理。

But the inherent errors of unstructured web and retrieval based on keyword matching limit distributed heterogeneous information resource sharing and automatic processing .

将AES对称加密与RSA非对称加密相结合，构建了一种数据库的混合加密方案；在该方案中，对敏感数据进行加密存储，明文关键字作为检索项。

Combined with the AES symmetric encryption and the RSA asymmetric encryption , we form a mixed encryption scheme . In this scheme , we only encrypt sensitive data , and the key words as expressly retrievals in database .

搜索引擎基于关键字的检索成为网页文本数据检索的主要方法。

Search engine keyword-based search into web page text of the main methods of data retrieval .

不仅实现了对图像进行基于关键字的检索，还实现了基于本质内容等多种组合方式的查询。

The process achieved not only keywords retrieval , but also content-based and methods above combined retrieval .

而现有的信息检索技术却存在很大的局限性，它仅提供了基于关键字的检索，而忽略了关键字本身所含的语义内容，无法满足用户极具个性化的查询需求。

The current information retrieval technologys just offer keywords-based searching , but ignore the semantic content of the keywords itself .

其中构件检索模块分为基于刻面的检索和基于关键字的检索，用户可以根据不同的需求选择不同的检索方式。

The component retrieval module is including the keyword retrieval and the facet retrieval and the user can choose based on their demand .

为了验证本文提出的语义检索模型的有效性，将基于本体的语义检索模型同传统的基于关键字的检索模型进行对比实验。

In order to test the performance of the semantic retrieval model , this paper does experiments by comparing this model with traditional model .

按照一个或多个列对数据进行分组后，可以使用it关键字来检索每个分组中的各个记录。

When you have grouped data by one or more columns , you can retrieve the individual records in each grouping by using the it keyword .

现有的图像检索系统大多只提供了基于图像的描述关键字的检索，却忽略了另一个重要的信息来源&图像的内容。

Most of The present image retrieval systems only concern the key word of the image description , neglecting another important element of image - the content of image .

在类库模块中，按照对象类模板描述对象类，管理和存储已有的对象类资源，并提供基于语义和基于关键字的检索功能。

In requirements analysis module , existed object class resources are managed and stored by describing in object class template . Searching function based on semantics or keywords is provided .