主题爬虫研究进展

随着互联网技术的飞速发展,网页数量急剧增加,搜索引擎的地位已经不可取代,成为人们使用Internet的入口。网络蜘蛛作为搜索引擎的信息来源是搜索引擎必不可少的组成部分。介绍网络蜘蛛设计中的关键技术。另外,随着用户个性化需求越来越强以及网页数量的急剧增加导致通用搜索引擎无法满足特定用户的需求,专业搜索引擎得到快速的发展。同时对于主题爬虫的研究也有很大的突破和进展。主题爬虫有别于通用爬虫,通用爬虫注重爬取的完整性,而主题爬虫强调网页与特定主题的相关性。同时对主题爬虫的研究现状进行介绍和总结。     

主题爬虫技术研究综述

随着移动互联网的普及以及网络信息指数的增长,如何有效地提取和利用这些信息面临巨大挑战。介绍了主题爬虫的工作原理、分类;回顾了近年来国内外关于主题爬虫的研究状况,分析了各种主题相似度的方法以及搜索策略,得出相比于普通的爬虫系统,基于网页内容和基于链接分析的爬虫系统的查准率、查全率都大幅度地提升;最后分析比较了主题网络爬虫两种动态搜索策略并指出了未来研究方向。     

聚焦爬虫常见算法分析

聚焦爬虫搜集与特定主题相关的页面,为搜索引擎构建页面集。传统的聚焦爬虫采用向量空间模型和局部搜索算法,精确率和召回率都比较低。文章分析了聚焦爬虫存在的问题及其相应的解决方法。最后对未来的研究方向进行了展望。     

基于HTMLParser信息提取的网络爬虫设计

无论是通用搜索还是垂直搜索,其关键的核心技术之一就是网络爬虫的设计.本文结合HTMLParser信息提取方法,对生活类垂直搜索引擎中网络爬虫进行了详细研究.通过深入分析生活类网站网址的树形结构的构架,开发了收集种子页面URL的模拟搜索器,并基于HTMLParser的信息提取方法,从种子页面中提取出与生活类主题相关的目标URL.经实验测试证明该爬虫的爬准率达93.552%,爬全率达96.720%,表明该网络爬虫是有效的,达到中等规模的垂直搜索企业级应用的要求.     

一种基于语义本体的网络爬虫模型

针对传统通用网络爬虫的自身固有的缺陷,结合本体的相关理论,提出了一种基于语义本体的网络爬虫的相关模型。该模型以本体构建领域知识概念集,结合知网,从语义的角度,利用扩展的元数据,在词的语义层次,对抓取的页面链接进行语义相关性计算,预测与主题相关的URL,提高采集的网络资源信息与设定主题的相关度。实验结果表明,该模型同其它通用网络爬虫模型相比具有较高的信息抓取准确率。     

基于朴素贝叶斯算法的主题爬虫的研究

主题爬虫是实现主题搜索引擎的关键部分。提出了利用朴素贝叶斯算法进行主题识别的方法,介绍了主题爬虫实现过程中所涉及到的关键部分,包括种子URL集合的生成、页面分析及特征提取、主题识别等。将基于朴素贝叶斯算法的主题爬虫,与基于链接分析的主题爬虫和基于主题词表的主题爬虫进行比较,实验表明基于朴素贝叶斯算法的主题爬虫准确性较好,论证了方法的可行性,为主题信息的采集奠定了良好的基础。     

垂直搜索引擎之主题网络爬虫

21世纪的今天,网络信息量呈几何倍数的增长。传统的通用搜索引擎已经不能很好的满足我们对特定信息的需求。所以,针对某一特定领域、某一特定人群或某一特定需求提供的有一定价值的信息和相关服务的垂直搜索引擎应运而生。而主题网络爬虫则是为垂直搜索引擎提供数据的自动化程序。本文主要研究了主题爬虫的各种搜索策略,指出了其中的劣以及最后对将来的发展做出了一个展望。     

基于贝叶斯分类器的主题爬虫研究*

主题爬虫是实现定题搜索引擎的核心技术。提出了基于贝叶斯分类器实现主题爬虫的方法,介绍了基于贝叶斯分类器的主题爬虫的系统结构以及系统关键部分的实现,包括URL队列、爬行历史、页面下载以及页面分析,并重点介绍了基于贝叶斯分类器的主题相关度算法。爬虫使用改进的TF-IDF算法来提取网页内容的特征,并采用贝叶斯分类器计算其主题相关度。实验结果表明,在搜索大量网络资源的情况下,贝叶斯分类器比PageRank算法更适合用于实现主题爬虫。     

基于URL模式集的主题爬虫

为提高主题爬虫的性能,依据站点信息组织的特点和URL的特征,提出一种基于URL模式集的主题爬虫。爬虫分两阶段,在实验爬虫阶段,采集站点样本数据,采用基于URL前缀树的模式构建算法构建URL模式,形成模式关系图,并利用HITS算法分析该模式关系图,计算出各模式的重要度;在聚焦爬虫阶段,无需预先下载页面,即可利用生成的URL模式判断页面是否主题相关和能否指导爬虫深入抓取,并根据URL模式的重要度预测待抓取链接优先级。实验表明,该爬虫相比现有的主题爬虫能快速引导爬虫抓取主题相关页面,保证爬虫的查准率和查全率,有效提高爬虫抓取效率。     

主题搜索引擎网络爬虫搜索策略的研究与实现

根据网络页面结构的特点,提出通过页面之间的主题传递来预测页面主题相关性的方法,解决了主题爬虫通道堵塞,抓取遗漏的问题。首先根据锚文本传递一个相关性信息值,如果锚文本给出的信息是相关,相关阈值就直接传递;如果是不相关,就乘以遗传基因比例之后传递。传递的过程中如果遇到相关的网页就恢复链接的相关性信息值到初始值。最后根据实验结果验证了算法的查全率与查准率,查全率有显著的提高。     

基于动态隧道技术的主题爬行策略

互联网网页所形成的主题孤岛严重影响了搜索引擎系统的主题爬虫性能,通过人工增加大量的初始种子链接来发现新主题的方法无法保证主题网页的全面性.在分析传统基于内容分析、基于链接分析和基于语境图的主题爬行策略的基础上,提出了一种基于动态隧道技术的主题爬虫爬行策略.该策略结合页面主题相关度计算和URL链接相关度预测的方法确定主题孤岛之间的网页页面主题相关性,并构建层次化的主题判断模型来解决主题孤岛之间的弱链接问题.同时,该策略能有效防止主题爬虫因采集过多的主题无关页面而导致的主题漂移现象,从而可以实现在保持主题语义信息的爬行方向上的动态隧道控制.实验过程利用主题网页层次结构检测页面主题相关性并抽取“体育”主题关键词,然后以此对采集的主题网页进行索引查询测试.结果表明,基于动态隧道技术的爬行策略能够较好的解决主题孤岛问题,明显提升了“体育”主题搜索引擎的准确率和召回率.     

A Genre-Aware Approach to Focused Crawling

Focused crawlers have as their main goal to crawl Web pages that are relevant to a specific topic or user interest, playing an important role for a great variety of applications. In general, they work by trying to find and crawl all kinds of pages deemed as related to an implicitly declared topic. However, users are often not simply interested in any document about a topic, but instead they may want only documents of a given type or genre on that topic to be retrieved. In this article, we describe an approach to focused crawling that exploits not only content-related information but also genre information present in Web pages to guide the crawling process. This approach has been designed to address situations in which the specific topic of interest can be expressed by specifying two sets of terms, the first describing genre aspects of the desired pages and the second related to the subject or content of these pages, thus requiring no training or any kind of preprocessing. The effectiveness, efficiency and scalability of the proposed approach are demonstrated by a set of experiments involving the crawling of pages related to syllabi of computer science courses, job offers in the computer science field and sale offers of computer equipments. These experiments show that focused crawlers constructed according to our genre-aware approach achieve levels of F1 superior to 88%, requiring the analysis of no more than 65% of the visited pages in order to find 90% of the relevant pages. In addition, we experimentally analyze the impact of term selection on our approach and evaluate a proposed strategy for semi-automatic generation of such terms. This analysis shows that a small set of terms selected by an expert or a set of terms specified by a typical user familiar with the topic is usually enough to produce good results and that such a semi-automatic strategy is very effective in supporting the task of selecting the sets of terms required to guide a crawling process.     

支持向量机在化学主题爬虫中的应用

爬虫是搜索引擎的重要组成部分,它沿着网页中的超链接自动爬行,搜集各种资源。为了提高对特定主题资源的采集效率,文本分类技术被用来指导爬虫的爬行。本文把基于支持向量机的文本自动分类技术应用到化学主题爬虫中,通过SVM 分类器对爬行的网页进行打分,用于指导它爬行化学相关网页。通过与基于广度优先算法的非主题爬虫和基于关键词匹配算法的主题爬虫的比较,表明基于SVM分类器的主题爬虫能有效地提高针对化学Web资源的采集效率。     

Finding seeds to bootstrap focused crawlers

Focused crawlers are effective tools for applications requiring a high number of pages belonging to a specific topic. Several strategies for implementing these crawlers have been proposed in the literature, which aim to improve crawling efficiency by increasing the number of relevant pages retrieved while avoiding non-relevant pages. However, an important aspect of these crawlers has been largely overlooked: the selection of the seed pages that serve as the starting points for a crawl. In this paper, we show that the seeds can greatly influence the performance of crawlers, and propose a new framework for automatically finding seeds. We describe a system that implements this framework and show, through a detailed experimental evaluation, that by providing crawlers a seed set that is large and varied, they not only obtain higher harvest rates but also an improved topic coverage.     

基于遗传算法的定题信息搜索策略

定题检索将信息检索限定在特定主题领域,提供主题领域内信息的检索服务。它是新一代搜索引擎的发展方向之一。定题检索的关键技术是主题相关信息的搜索。本文提出了基于遗传算法的定题信息搜索策略,提高链接于内容相似度不高的网页之后的页面被搜索的机会,扩大了相关网页的搜索范围。同时,借助超链Metadata的提示信息预测链接页面的主题相关度,加快了搜索速度。对比搜索试验证明了算法具有较好的性能。     

Improving the performance of focused web crawlers

This work addresses issues related to the design and implementation of focused crawlers. Several variants of state-of-the-art crawlers relying on web page content and link information for estimating the relevance of web pages to a given topic are proposed. Particular emphasis is given to crawlers capable of learning not only the content of relevant pages (as classic crawlers do) but also paths leading to relevant pages. A novel learning crawler inspired by a previously proposed Hidden Markov Model (HMM) crawler is described as well. The crawlers have been implemented using the same baseline implementation (only the priority assignment function differs in each crawler) providing an unbiased evaluation framework for a comparative analysis of their performance. All crawlers achieve their maximum performance when a combination of web page content and (link) anchor text is used for assigning download priorities to web pages. Furthermore, the new HMM crawler improved the performance of the original HMM crawler and also outperforms classic focused crawlers in searching for specialized topics.     

An effective and efficient Web content extractor for optimizing the crawling process

Classical Web crawlers make use of only hyperlink information in the crawling process. However, focused crawlers are intended to download only Web pages that are relevant to a given topic by utilizing word information before downloading the Web page. But, Web pages contain additional information that can be useful for the crawling process. We have developed a crawler, iCrawler (intelligent crawler), the backbone of which is a Web content extractor that automatically pulls content out of seven different blocks: menus, links, main texts, headlines, summaries, additional necessaries, and unnecessary texts from Web pages. The extraction process consists of two steps, which invoke each other to obtain information from the blocks. The first step learns which HTML tags refer to which blocks using the decision tree learning algorithm. Being guided by numerous sources of information, the crawler becomes considerably effective. It achieved a relatively high accuracy of 96.37% in our experiments of block extraction. In the second step, the crawler extracts content from the blocks using string matching functions. These functions along with the mapping between tags and blocks learned in the first step provide iCrawler with considerable time and storage efficiency. More specifically, iCrawler performs 14 times faster in the second step than in the first step. Furthermore, iCrawler significantly decreases storage costs by 57.10% when compared with the texts obtained through classical HTML stripping. Copyright © 2013 John Wiley & Sons, Ltd.     

基于概率模型的主题爬虫的研究和实现

在现有多种主题爬虫的基础上,提出了一种基于概率模型的主题爬虫。它综合抓取过程中获得的多方面的特征信息来进行分析,并运用概率模型计算每个URL的优先值,从而对URL进行过滤和排序。基于概率模型的主题爬虫解决了大多数爬虫抓取策略单一这个缺陷,它与以往主题爬虫的不同之处是除了使用主题相关度评价指标外,还使用了历史评价指标和网页质量评价指标,较好地解决了"主题漂移"和"隧道穿越"问题,同时保证了资源的质量。最后通过多组实验验证了其在主题网页召回率和平均主题相关度上的优越性。     

基于动态主题库的主题爬虫

通过对基于不同策略过滤URL的主题爬虫的研究,提出了一种基于动态主题库的主题爬虫.它能够在运行期间实时地更新主题库,提高了对URL过滤的准确度.实验表明,所提的主题爬虫能够在相对较少的时间中,检索尽量少的网络空间,抓取到较多与主题相关的网页.     

聚焦爬虫技术研究综述

因特网的迅速发展对万维网信息的查找与发现提出了巨大的挑战。对于大多用户提出的与主题或领域相关的查询需求,传统的通用搜索引擎往往不能提供令人满意的结果网页。为了克服通用搜索引擎的以上不足,提出了面向主题的聚焦爬虫的研究。至今,聚焦爬虫已成为有关万维网的研究热点之一。文中对这一热点研究进行综述,给出聚焦爬虫(Focused Crawler)的基本概念,概述其工作原理;并根据研究的发展现状,对聚焦爬虫的关键技术(抓取目标描述,网页分析算法和网页搜索策略等)作系统介绍和深入分析。在此基础上,提出聚焦爬虫今后的一些研究方向,包括面向数据分析和挖掘的爬虫技术研究,主题的描述与定义,相关资源的发现,W eb数据清洗,以及搜索空间的扩展等。