基于Web中文检索系统SEARCH2000的设计与实现

本文详细介绍Search 2 0 0 0中文检索系统的设计思想及实现方法。与传统的全文检索系统相比 ,基于WEB的信息检索系统 ,具有许多全新的特征。页面为半结构化文档、页面通过超链接相互关联、页面的内容覆盖不同应用领域并且拥有大量专有名词和缩略词汇 ,这些特性成为影响查询精度的主要因素。针对Web的上述特性设计的Search2 0 0 0全文检索系统 ,使用智能化的页面相关分析、评分技术 ,以及高效数据存取、压缩算法和知识库的支持 ,使其具有使用方便、查询时间短、查询精度高等特点。

Method of acquiring web features and its application in web search

Focusing on the problem that it is hard to utilize the web multi-fields information with various forms in large scale web search,a novel approach,which can automatically acquire features from web pages based on a set of well defined rules,is proposed.The features describe the contents of web pages from different aspects and they can be used to improve the ranking performance for web search.The acquired feature has the advantages of unified form and less noise,and can easily be used in web page relevance ranking.A special specs for judging the relevance between user queries and acquired features is also proposed.Experimental results show that the features acquired by the proposed approach and the feature relevance specs can significantly improve the relevance ranking performance for web search.

一种针对websearch应用的缓存替换算法

本文通过对websearch负载的分析,总结出负载访问模式的特点,在此基础上提出了一种新的缓存替换算法——ERDP-LRU.与传统的LRU算法的区别是它采用基于重用距离的放置策略.通过模拟实验和实际系统验证,在各种不同的典型负载和缓存大小下,ERDP-LRU的效果均好于其它替换算法.

基于地理-时间意图和偏好的个性化Web搜索框架GT-WSearch

基于Web查询的地理位置、时间查询意图和用户偏好的个性化Web搜索可以改善Web搜索结果,更好地满足不同用户的信息需求。提出了GT-WSearch个性化Web搜索框架,它通过挖掘搜索结果、用户点击数据和对查询进行分析得到的用户概貌和查询概貌,来捕捉用户的地理-时间的意图和偏好,提高搜索质量。用户概貌表明了查询自身的地理-时间的特性。GT-WSearch框架在排序函数中利用文档的地理位置、时间的相关度来进行个性化搜索。最后将使用线性的相关度排序函数进行重新排序的搜索结果返回给用户。大量实验结果表明,所提出的个性化方法在提高Web搜索结果的质量中取得了明显的效果。

Ontology mapping approach using web search engine

A new mapping approach for automated ontology mapping using web search engines （such as Google） is presented. Based on lexico-syntactic patterns, the hyponymy relationships between ontology concepts can be obtained from the web by search engines and an initial candidate mapping set consisting of ontology concept pairs is generated. According to the concept hierarchies of ontologies, a set of production rules is proposed to delete the concept pairs inconsistent with the ontology semantics from the initial candidate mapping set and add the concept pairs consistent with the ontology semantics to it. Finally, ontology mappings are chosen from the candidate mapping set automatically with a mapping select rule which is based on mutual information. Experimental results show that the F-measure can reach 75% to 100% and it can effectively accomplish the mapping between ontologies.

An Efficient Multi-Keyword Query Processing Strategy on P2P Based Web Search

The paper presents a novel benefit based query processing strategy for efficient query routing. Based on DHT as the overlay network, it first applies Nash equilibrium to construct the optimal peer group based on the correlations of keywords and coverage and overlap of the peers to decrease the time cost, and then presents a two-layered architecture for query processing that utilizes Bloom filter as compact representation to reduce the bandwidth consumption. Extensive experiments conducted on a real world dataset have demonstrated that our approach obviously decreases the processing time, while improves the precision and recall as well.

The Study on China’s Flu Prediction Model Based on Web Search Data

Influenza is a kind of infectious disease, which spreads quickly and widely. The outbreak of influenza has brought huge losses to society. In this paper, four major categories of flu keywords, “prevention phase”, “symptom phase”, “treatment phase”, and “commonly-used phrase” were set. Python web crawler was used to obtain relevant influenza data from the National Influenza Center’s influenza surveillance weekly report and Baidu Index. The establishment of support vector regression (SVR), least absolute shrinkage and selection operator (LASSO), convolutional neural networks (CNN) prediction models through machine learning, took into account the seasonal characteristics of the influenza, also established the time series model (ARMA). The results show that, it is feasible to predict influenza based on web search data. Machine learning shows a certain forecast effect in the prediction of influenza based on web search data. In the future, it will have certain reference value in influenza prediction. The ARMA(3,0) model predicts better results and has greater generalization. Finally, the lack of research in this paper and future research directions are given.

Weighted PageRank Algorithm Search Engine Ranking Model for Web Pages

As data grows in size,search engines face new challenges in extracting more relevant content for users’searches.As a result,a number of retrieval and ranking algorithms have been employed to ensure that the results are relevant to the user’s requirements.Unfortunately,most existing indexes and ranking algo-rithms crawl documents and web pages based on a limited set of criteria designed to meet user expectations,making it impossible to deliver exceptionally accurate results.As a result,this study investigates and analyses how search engines work,as well as the elements that contribute to higher ranks.This paper addresses the issue of bias by proposing a new ranking algorithm based on the PageRank(PR)algorithm,which is one of the most widely used page ranking algorithms We pro-pose weighted PageRank(WPR)algorithms to test the relationship between these various measures.The Weighted Page Rank(WPR)model was used in three dis-tinct trials to compare the rankings of documents and pages based on one or more user preferences criteria.Thefindings of utilizing the Weighted Page Rank model showed that using multiple criteria to rankfinal pages is better than using only one,and that some criteria had a greater impact on ranking results than others.

Web Search Query Privacy, an End-User Perspective

While search engines have become vital tools for searching information on the Internet, privacy issues remain a growing concern due to the technological abilities of search engines to retain user search logs. Although such capabilities might provide enhanced personalized search results, the confidentiality of user intent remains uncertain. Even with web search query obfuscation techniques, another challenge remains, namely, reusing the same obfuscation methods is problematic, given that search engines have enormous computation and storage resources for query disambiguation. A number of web search query privacy procedures involve the cooperation of the search engine, a non-trusted entity in such cases, making query obfuscation even more challenging. In this study, we provide a review on how search engines work in regards to web search queries and user intent. Secondly, this study reviews material in a manner accessible to those outside computer science with the intent to introduce knowledge of web search engines to enable non-computer scientists to approach web search query privacy innovatively. As a contribution, we identify and highlight areas open for further investigative and innovative research in regards to end-user personalized web search privacy—that is methods that can be executed on the user side without third party involvement such as, search engines. The goal is to motivate future web search obfuscation heuristics that give users control over their personal search privacy.

Towards More Efficient Image Web Search

With the flood of information on the Web, it has become increasingly necessary for users to utilize automated tools in order to find, extract, filter, and evaluate the desired information and knowledge discovery. In this research, we will present a preliminary discussion about using the dominant meaning technique to improve Google Image Web search engine. Google search engine analyzes the text on the page adjacent to the image, the image caption and dozens of other factors to determine the image content. To improve the results, we looked for building a dominant meaning classification model. This paper investigated the influence of using this model to retrieve more efficient images, through sequential procedures to formulate a suitable query. In order to build this model, the specific dataset related to an application domain was collected;K-means algorithm was used to cluster the dataset into K-clusters, and the dominant meaning technique is used to construct a hierarchy model of these clusters. This hierarchy model is used to reformulate a new query. We perform some experiments on Google and validate the effectiveness of the proposed approach. The proposed approach is improved for in precision, recall and F1-measure by 57%, 70%, and 61% respectively.

Website Search Engine Optimization: Geographical and Cultural Point of View

The concept of Webpage visibility is usually linked to search engine optimization (SEO), and it is based on global in-link metric [1]. SEO is the process of designing Webpages to optimize its potential to rank high on search engines, preferably on the first page of the results page. The purpose of this research study is to analyze the influence of local geographical area, in terms of cultural values, and the effect of local society keywords in increasing Website visibility. Websites were analyzed by accessing the source code of their homepages through Google Chrome browser. Statistical analysis methods were selected to assess and analyze the results of the SEO and search engine visibility (SEV). The results obtained suggest that the development of Web indicators to be included should consider a local idea of visibility, and consider a certain geographical context. The geographical region that the researchers are considering in this research is the Hashemite kingdom of Jordan (HKJ). The results obtained also suggest that the use of social culture keywords leads to increase the Website visibility in search engines as well as localizes the search area such as google.jo, which localizes the search for HKJ.

Application of a Web Search Engine in Translating Business Terms in English-Chinese Dictionaries

Web search engines are important tools for lexicography.This paper takes translation of business terms("e-commerce"and"e-business")as an example to illustrate the application of web search engines in English-Chinese dictionary translation,including the methods of(1)finding the potential Chinese equivalents of the English business terms,and(2)selecting typical and proper Chinese equivalents in accordance with the frequencies and the meanings of the English business terms respectively.

Ranking of Web Pages in a Personalized Search

The basic idea behind a personalized web search is to deliver search results that are tailored to meet user needs, which is one of the growing concepts in web technologies. The personalized web search presented in this paper is based on exploiting the implicit feedbacks of user satisfaction during her web browsing history to construct a user profile storing the web pages the user is highly interested in. A weight is assigned to each page stored in the user’s profile;this weight reflects the user’s interest in this page. We name this weight the relative rank of the page, since it depends on the user issuing the query. Therefore, the ranking algorithm provided in this paper is based on the principle that;the rank assigned to a page is the addition of two rank values R_rank and A_rank. A_rank is an absolute rank, since it is fixed for all users issuing the same query, it only depends on the link structures of the web and on the keywords of the query. Thus, it could be calculated by the PageRank algorithm suggested by Brin and Page in 1998 and used by the google search engine. While, R_rank is the relative rank, it is calculated by the methods given in this paper which depends mainly on recording implicit measures of user satisfaction during her previous browsing history.

Stability-mutation feature identification of Web search keywords based on keyword concentration change ratio

Purpose: The aim of this paper is to discuss how the keyword concentration change ratio(KCCR) is used while identifying the stability-mutation feature of Web search keywords during information analyses and predictions.Design/methodology/approach: By introducing the stability-mutation feature of keywords and its significance, the paper describes the function of the KCCR in identifying keyword stability-mutation features. By using Ginsberg's influenza keywords, the paper shows how the KCCR can be used to identify the keyword stability-mutation feature effectively.Findings: Keyword concentration ratio has close positive correlation with the change rate of research objects retrieved by users, so from the characteristic of the 'stability-mutation' of keywords, we can understand the relationship between these keywords and certain information. In general, keywords representing for mutation fit for the objects changing in short-term, while those representing for stability are suitable for long-term changing objects. Research limitations: It is difficult to acquire the frequency of keywords, so indexes or parameters which are closely related to the true search volume are chosen for this study.Practical implications: The stability-mutation feature identification of Web search keywords can be applied to predict and analyze the information of unknown public events through observing trends of keyword concentration ratio.Originality/value: The stability-mutation feature of Web search could be quantitatively described by the keyword concentration change ratio(KCCR). Through KCCR, the authors took advantage of Ginsberg's influenza epidemic data accordingly and demonstrated how accurate and effective the method proposed in this paper was while it was used in information analyses and predictions.

基于图匹配的Web实体抽取算法研究

现今Web中存在大量缺失、不一致及不精确的数据,而传统的搜索引擎只能根据关键词返回文档片段,无法直接获取目标实体。提出一种新的基于图匹配的实体抽取算法GMEE(Graph Matching Based Entity Extraction),首先将片段按词语分割,进行实体的初步筛选;然后根据各实体之间的结构和语义关系建立“加权语义实体关联图”;最后利用“最大公共子图匹配”策略抽取目标实体。实验结果表明,提出的算法在不需要大量参数训练及传递的情况下,能够对抽取的实体集进行有效的精简,既保证了召回率、准确率,又提高了抽取过程的可解释性。

基于Web挖掘的智能门户搜索引擎的研究

搜索引擎是人们在Internet上快速获得信息的重要工具之一,但是由于中文自身的特点,使得检索结果的准确性和相关性不是很高,将Web挖掘技术应用到搜索引擎领域,从而产生智能搜索引擎,将会给用户提供一个高效、准确的Web检索工具。文章首先介绍了搜索引擎的工作原理和相关概念,然后介绍了Web挖掘的定义、分类和应用。最后,详细讨论了Web挖掘技术在智能搜索引擎的重要应用。

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 。

Web信息采集研究进展

As a basic component of search engine and a series of other services on Web,Web crawler is playing an important role. Roughly,a Web crawler is a program which automatically traverses the Web by downloading documents and following links from page to page. This article detailedly explains the principles and difficulties on the Web crawler,comprehensively argues several hot directions of Web crawler,and at last views the new direction of Web crawler.

网络爬虫在Web信息搜索与数据挖掘中应用

分析了万维网不良网络信息对网络文化安全带来的挑战,提出了Web信息搜索与数据挖掘体系结构,并介绍了该体系结构中的关键技术和运行原理。分析了普通爬虫所实现的功能和不足之后,重点论述了该爬虫的工作原理、实现方式和性能分析以及该爬虫不同于其它爬虫的功能和在Web信息搜索与数据挖掘体系中应用。通过试验测试表明,该爬虫能够很好地获取万维网上的各种信息资源,有助于网络文化内容监测与管理。