毕业论文外文翻译-信息管理系统.docx

毕业论文（设计）外文资料：Information management systemWiliam K.Thomson U.S.AAbstract:An information storage, searching and retrieval system for large (gigabytes) domains of archived textual dam. The system includes multiple query generation processes, a search process, and a presentation of search results that is sorted by category or type and that may be customized based on the professional discipline(or analogous personal characteristic of the user), thereby reducing the amount of time and cost required to retrieve relevant results. Keyword:Information management Retrieval system Object-Oriented 1.INTRUDUCTIONThis invention relates to an information storage, searching and retrieval system that incorporates a novel organization for presentation of search results from large (gigabytes) domains of archived textual data. 2.BACKGROUDN OF THE INVENTIONOn-line information retrieval systems are utilized for searching and retrieving many kinds of information. Most systems used today work in essentially the same manner; that is, users log on (through a computer terminal or personal microcomputer, and typically from a remote location), select a source of information (i.e., a particular database) which is usually something less than the complete domain, formulate a query, launch the search, and then review the search results displayed on the terminal or microcomputer, typically with documents (or summaries of documents) displayed in reverse chronological order. This process must be repeated each time another source (database) or group of sources is selected (which is frequently necessary in order to insure all relevant documents have been found).Additionally, this process places on the user the burden of organizing and assimilating the multiple results generated from the launch of the same query in each of the multiple sources (databases) that the user needs (or wants) to search. Present systems that allow searching of large domains require persons seeking information in these domains to attempt to modify their queries to reduce the search results to a size that the user can assimilate by browsing through them (thus, potentially eliminating relevant results). In many cases end users have been forced to use an intermediary (i.e., a professional searcher) because the current collections of sources are both complex and extensive, and effective search strategies often vary significantly from one source to another. Even with such guidance, potential relevant answers are missed because all potentially relevant databases or information sources are not searched on every query. Much effort has been expended on refining and improving source selection by grouping sources or database files together. Significant effort has also been expended on query formulation through the use of knowledge bases and natural language processing. However, as the groupings of sources become larger, and the responses to more comprehensive search queries become more complete, the person seeking information is often faced with the daunting task of sifting through large unorganized answer sets in an attempt to find the most relevant documents or information. 3.SUMMARY OF THE INVENTION The invention provides an information storage, searching and retrieval system for a large domain of archived data of various types, in which the results of a search are organized into discrete types of documents and groups of document types so that users may easily identify relevant information more efficiently and more conveniently than systems currently in use. The system of the invention includes means for storing a large domain of data contained in multiple source records, at least some of the source records being comprised of individual documents of multiple document types; means for searching substantially all of the domain with a single search query to identify documents responsive to the query; and means for categorizing documents responsive to the query based on document type, including means for generating a summary of the number of documents responsive to the query which fall within various predetermined categories of document types. The query generation process may contain a knowledge base including a thesaurus that has predetermined and embedded complex search queries, or use natural language processing, or fuzzy logic, or tree structures, or hierarchical relationship or a set of commands that allow persons seeking information to formulate their queries. The search process can utilize any index and search engine techniques including Boolean, vector, and probabilistic as long as a substantial portion of the entire domain of archived textual data is searched for each query and all documents found are returned to the organizing process. The sorting/categorization process prepares the search results for presentation by assembling the various document types retrieved by the search engine and then arranging these basic document types into sometimes broader categories that are readily understood by and relevant to the user.The search results are then presented to the user and arranged by category along with an indication as to the number of relevant documents found in each category. The user may then examine search results in multiple formats, allowing the user to view as much of the document as the user deems necessary. 4.BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a block diagram illustrating an information retrieval system of the invention; FIG. 2 is a diagram illustrating a query formulation and search process utilized in the invention; FIG. 3 is a diagram illustrating a sorting process for organizing and presenting search results.5.BEST MODE FOR CARRYING OUT THE INVENTION As is illustrated in the block diagram of FIG. 1 , the information retrieval system of the invention includes an input/output process ,a query generation process, a search process that involves a large domain of textual data (typically in the multiple gigabyte range), an organizing process, presentation of the information to the user, and a process to identify and characterize the types of documents contained in the large domain of data.Turning now to FIG. 2, the query generation process preferably includes a knowledge base containing a thesaurus and a note pad, and preferably utilizes embedded predefined complex Boolean strategies. Such a system allows the user to enter their description of the information needed using simple words/phrases made up of "natural" language and to rely on the system to assist in generating the full search query, which would include, e.g., synonyms and alternate phraseology. The user can then request, by a command such as "VI CO 1", to view the complete document selected from the list, giving, in this case, complete information about the identity and credentials of the expert.FIG. 3 illustrates how five typical sources of information (i.e., source records) can be sorted into many document types and then subsequently into categories. For example, a typical trade magazine may contain several types of information such as editorials, regular columns, feature articles, news, product announcements, and a calendar of events. Thus, the trade magazine (i.e., the source record) may be sorted into these various document types, and these document types in turn may be categorized or grouped into categories contained in one or more sets of categories; each document type typically will be sorted into one category within a set of categories, but the individual categories within each set will vary from one set to another. For example, one set of categories may be established for a first characteristic type of user, and a different set of categories may be established for a second characteristic type of user. When a user corresponding to type #1 executes a search, the system automatically utilizes the categories of set #1, corresponding to that particular type of user, in organizing the results of the search for review by the user. When a user from type #2 executes a search, however, the system automatically utilizes the categories of set #2 in presenting the search results to the user.The information storage, searching and retrieval system of the invention resolves the common difficulties in typical on-line information retrieval systems that operate on large (e.g., 2 gigabytes or more) domains of textual data, query generation, source selection, and organizing search results. The information base with the thesaurus and embedded search strategies allows users to generate expert search queries in their own "natural" language. Source (i.e., database) selection is not an issue because the search engines are capable of searching substantially the entire domain on every query. Moreover, the unique presentation of search results by category set substantially reduces the time and cost of performing repetitive searches in multiple databases and therefore of efficiently retrieving relevant search results.While a preferred embodiment of the present invention has been described, it should be understood that various changes, adaptations and modifications may be made therein without departing from the spirit of the invention and the scope of the appended claims.中文译文：信息管理系统Wiliam K.Thomson U.S.A摘要：一个信息存储，查询和检索系统主要应用于大(千兆字节)的需要存档的文字领域。该系统包括多个查询产生过程和一个搜索过程。而查询的结果一般是按类别和类型进行排序的，检索字段是由个人决定的，在查询的过程中，可能基于这个搜索结果查看到多个相关的信息（或类似的用户个人特点介绍），从而减少了搜索结果是所需的时间和费用。关键词：信息管理；检索系统；面向对象1. 简介信息的存储，查询和检索系统,主要应用原文档数据比较大的文档，利用搜索条件和索引字段可以快速查询结果。2. 开发背景网上查询系统主要用于查询和检索在线的各种各样的信息。今天所使用的多数系统实际上采用的是同一方式。也就是说，用户登录（通过计算机终端或个人微机，或者是远程登录），选择一个信息源（比如一个特定的数据库），通常是一些不完整的检索条件，开始查询，启动搜索，然后查询结果将显示在计算机终端或个人微机上，且查询结果一般按照时间的顺序显示。在查询过程中，会不断的重复查询每一个数据来源或一组数据源，为了确保搜索出所有相关的文件，这个重复是非常必要的。另外，这个查询过程也给用户带来一定的负担，他要根据从同一个数据源查询出的多个结果，进行归纳和总结。而目前的系统可以搜寻大的数据，在这过程中要求人们寻求信息或试图修改他们的查询条件，以减少不必要的搜索结果（消灭潜在的相关结果），使用户查询到真正要查的数据。在许多情况下，用户被迫使用中介（例如专业的搜索引擎），因为当前收藏的来源是复杂和广泛的，并且有效的搜索策略经常从一个数据来源变化到另一个。即使你按照这样操作，也有可能错过相关的答案，因为所有可能相关的数据库或信息来源并不在每一次搜索查询中。所以就要付出很大的努力改善和提高数据源的选择，更大的努力在操作查询时所制定的数据库语言。然而，当面对变得更大来源分组或需要更加全面的查询结果时，这个问题就更加明显，人们寻找的信息经常面对大量未组织的结果集合，这样就需要增加过滤查询的重要任务。3. 系统概要该系统主要应用于对大量数据进行信息存储，查询和检索，查询的结果将被导出成文件类型，比目前的系统更方面，容易的找到用户想要查询的有关数据。该系统不仅包括存储广泛数据领域的复合数据源记录，还包括多个文件类型的某些原始记录。该方式提供了搜索大数据领域所进行的一次唯一辨认文件的重要查询部分；还提供了文件重要部分的查询，以及包括对文件数量的统计和属于各种各样的预先确定类别的文件查询。查询创建过程包含一个知识库，该知识库包括被预先确定和嵌入复杂查询的分类词典，或者是自然语言的处理，或者模糊逻辑，或者树型结构，或者等级关系，或者是一套寻求信息的公式化查询命令。搜索的过程可能利用到所有的索引和搜索引擎技术，包括布尔，传播媒介，机率查询。只要每次查询到一个原文归档数据的固有部分，所有建立的文档就能返回到其组织过程。排序或分类的过程是通过调用搜索引擎检索查询的结果，从而为引入各种各样的基本文件类型做准备，然后组织安排这些容易被理解且与用户密切相关的基本文件类型。然后提供给相对于用户相关查询的结果与在该查询结果中的每个类别相关文档数量的统计。用户可以以多种形式来检查查询的结果，并且用户可以根据自己的需要来查看相关的文件。4. 图例简要说明图1是信息查询系统总流程图；图2是系统制定查询和搜索过程图;图3是查询排序过程中组织和显示结果5. 该系统的最佳模式正如图1所说明的那样，信息检索系统的开发包括一个输入、输出过程，一个查询创建过程，一个大量数据范围的查询过程(典型地在多个千兆字节范围)，一个用户信息的组织过程，以及一个辨认和描绘在大数据领域中文件的类型。如图2，查询生成过程包括分类词词典和笔记的一个知识库和运用嵌入被定定义的复杂战略。这样系统允许用户输入简单的词或词组，并且需要的他们的信息的描述由“自然”语言组成和依靠系统协助引起充分的查询，将包括同义词和供选择文词。用户发出一个命令然后请求，例如“VI CO 1”，查验从名单挑选的完全文件，在这种情况下，给关于身分专家的完全信息和证件。图3说明了五种一般的信息源(即原始记录)可以被写入多数类型的文档，随后被写入类。例如，一本典型的商业杂志也许包含信息的几个类型，例如社论、规则专栏、特写、新闻、产品公告和事件日历。因此，商业杂志(即原始记录)也许被排序入各种各样的文件类型和这些文件类型也许反过来被分类或被编组入一个或更多套包含的类别， 每个文件类型在一套将典型地被排序入一个类别之内，但各自的类别在每个集合之内从一个集合将变化到另一个。例如，一套类别为用户的第一个典型类型建立，并且不同的套类别也许为用户的第二个典型类型建立。当对应类型#1的用户执行一次查询时，系统为回顾自动地运用集合#1类别，对应于用户的那个特殊类型，在由用户组织查询的结果。当一名用户从类型#2执行一次查询时，系统提出查询结果自动地运用集合#2类别对用户。信息存储、搜索和检索系统的开发解决了原文数据、查询方案、资源选择和组织查询结果等大容量数据范围 (即二十亿字节或更多)的在线信息检索系统的基本难题。基于分类词典和嵌入搜索策略的信息库，允许用户使用“自然”语言来进行专业的信息查询。数据来源(如数据库)的选择已不再是个问题，因为搜索引擎能够在每次搜索时可以搜索到整个数据域。查询结果的独特类设置介绍不但极大地减少了反复查询多个数据库所付出的时间和费用，并且可以做到高效率检索相关的查询结果。当现有开发系统被具体化描述时，应该不能摒弃该开发系统的精髓和附加规范，便可以了解到所开发的系统中各式各样的变化、适应和改动。五分钟搞定5000字毕业论文外文翻译，你想要的工具都在这里!在科研过程中阅读翻译外文文献是一个非常重要的环节，许多领域高水平的文献都是外文文献，借鉴一些外文文献翻译的经验是非常必要的。由于特殊原因我翻译外文文献的机会比较多，慢慢地就发现了外文文献翻译过程中的三大利器：Google“翻译”频道、金山词霸（完整版本）和CNKI“翻译助手"。具体操作过程如下： 1.先打开金山词霸自动取词功能，然后阅读文献； 2.遇到无法理解的长句时，可以交给Google处理，处理后的结果猛一看，不堪入目，可是经过大脑的再处理后句子的意思基本就明了了； 3.如果通过Google仍然无法理解，感觉就是不同，那肯定是对其中某个“常用单词”理解有误，因为某些单词看似很简单，但是在文献中有特殊的意思，这时就可以通过CNKI的“翻译助手”来查询相关单词的意思，由于CNKI的单词意思都是来源与大量的文献，所以它的吻合率很高。 另外，在翻译过程中最好以“段落”或者“长句”作为翻译的基本单位，这样才不会造成“只见树木，不见森林”的误导。四大工具： 1、Google翻译： google，众所周知，谷歌里面的英文文献和资料还算是比较详实的。我利用它是这样的。一方面可以用它查询英文论文，当然这方面的帖子很多，大家可以搜索，在此不赘述。回到我自己说的翻译上来。下面给大家举个例子来说明如何用吧比如说“电磁感应透明效应”这个词汇你不知道他怎么翻译，首先你可以在CNKI里查中文的，根据它们的关键词中英文对照来做，一般比较准确。 在此主要是说在google里怎么知道这个翻译意思。大家应该都有词典吧，按中国人的办法，把一个一个词分着查出来，敲到google里，你的这种翻译一般不太准，当然你需要验证是否准确了，这下看着吧，把你的那支离破碎的翻译在google里搜索，你能看到许多相关的文献或资料，大家都不是笨蛋，看看，也就能找到最精确的翻译了，纯西式的！我就是这么用的。 2、CNKI翻译： CNKI翻译助手，这个网站不需要介绍太多，可能有些人也知道的。主要说说它的有点，你进去看看就能发现：搜索的肯定是专业词汇，而且它翻译结果下面有文章与之对应（因为它是CNKI检索提供的，它的翻译是从文献里抽出来的），很实用的一个网站。估计别的写文章的人不是傻子吧，它们的东西我们可以直接拿来用，当然省事了。网址告诉大家，有兴趣的进去看看，你们就会发现其乐无穷！还是很值得用的。 3、网路版金山词霸（不到1M）： 4、有道在线翻译：翻译时的速度：这里我谈的是电子版和打印版的翻译速度，按个人翻译速度看，打印版的快些，因为看电子版本一是费眼睛，二是如果我们用电脑，可能还经常时不时玩点游戏，或者整点别的，导致最终SPPEED变慢，再之电脑上一些词典（金山词霸等）在专业翻译方面也不是特别好，所以翻译效果不佳。在此本人建议大家购买清华大学编写的好像是国防工业出版社的那本英汉科学技术词典，基本上挺好用。再加上网站如：google CNKI翻译助手，这样我们的翻译速度会提高不少。具体翻译时的一些技巧（主要是写论文和看论文方面） 大家大概都应预先清楚明白自己专业方向的国内牛人，在这里我强烈建议大家仔细看完这些头上长角的人物的中英文文章，这对你在专业方向的英文和中文互译水平提高有很大帮助。 我们大家最蹩脚的实质上是写英文论文，而非看英文论文，但话说回来我们最终提高还是要从下大工夫看英文论文开始。提到会看，我想它是有窍门的，个人总结如下： 1、把不同方面的论文分夹存放，在看论文时，对论文必须做到看完后完全明白（你重视的论文）；懂得其某部分讲了什么（你需要参考的部分论文），在看明白这些论文的情况下，我们大家还得紧接着做的工作就是把论文中你觉得非常巧妙的表达写下来，或者是你论文或许能用到的表达摘记成本。这个本将是你以后的财富。你写论文时再也不会为了一些表达不符合西方表达模式而烦恼。你的论文也降低了被SCI或大牛刊物退稿的几率。不信，你可以试一试 2、把摘记的内容自己编写成检索，这个过程是我们对文章再回顾，而且是对你摘抄的经典妙笔进行梳理的重要阶段。你有了这个过程。写英文论文时，将会有一种信手拈来的感觉。许多文笔我们不需要自己再翻译了。当然前提是你梳理的非常细，而且中英文对照写的比较详细。 3、最后一点就是我们往大成修炼的阶段了，万事不是说成的，它是做出来的。写英文论文也就像我们小学时开始学写作文一样，你不练笔是肯定写不出好作品来的。所以在此我鼓励大家有时尝试着把自己的论文强迫自己写成英文的，一遍不行，可以再修改。最起码到最后你会很满意。呵呵，我想我是这么觉得的。12