融合本体和改进禁忌搜索策略的气象灾害主题爬虫方法

针对传统主题爬虫方法容易陷入局部最优和主题描述不足的问题,提出一种融合本体和改进禁忌搜索策略（On-ITS）的主题爬虫方法。首先利用本体语义相似度计算主题语义向量,基于超级文本标记语言（HTML）网页文本特征位置加权构建网页文本特征向量,然后采用向量空间模型计算网页的主题相关度。在此基础上,计算锚文本主题相关度以及链接指向网页的PR值,综合分析链接优先度。另外,为了避免爬虫陷入局部最优,设计了基于ITS的主题爬虫,优化爬行队列。以暴雨灾害和台风灾害为主题,在相同的实验环境下,基于On-ITS的主题爬虫方法比对比算法的爬准率最多高58%,最少高8%,其他评价指标也很好。基于On-ITS的主题爬虫方法能有效提高获取领域信息的准确性,抓取更多与主题相关的网页。     

基于主题相关概念和网页分块的主题爬虫研究

针对传统主题爬虫的不足, 提出一种基于主题相关概念和网页分块的主题爬虫。先通过主题分类树获取主题相关概念集合, 然后结合主题描述文档构建主题向量来描述主题; 下载网页后引入网页分块来穿越“灰色隧道”; 采用文本内容和链接结构相结合的策略计算候选链接优先级, 并在HITS算法的基础上提出了R-HITS算法计算链接结构对候选链接优先级的贡献。实验结果表明, 利用该方法实现的主题爬虫查准率达66%、信息量总和达53%, 在垂直搜索引擎和舆情分析应用方面有更好的搜索效果。     

Link contexts in classifier-guided topical crawlers

Context of a hyperlink or link context is defined as the terms that appear in the text around a hyperlink within a Web page. Link contexts have been applied to a variety of Web information retrieval and categorization tasks. Topical or focused Web crawlers have a special reliance on link contexts. These crawlers automatically navigate the hyperlinked structure of the Web while using link contexts to predict the benefit of following the corresponding hyperlinks with respect to some initiating topic or theme. Using topical crawlers that are guided by a support vector machine, we investigate the effects of various definitions of link contexts on the crawling performance. We find that a crawler that exploits words both in the immediate vicinity of a hyperlink as well as the entire parent page performs significantly better than a crawler that depends on just one of those cues. Also, we find that a crawler that uses the tag tree hierarchy within Web pages provides effective coverage. We analyze our results along various dimensions such as link context quality, topic difficulty, length of crawl, training data, and topic domain. The study was done using multiple crawls over 100 topics covering millions of pages allowing us to derive statistically strong results.     

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.     

基于遗传算法的主题爬虫

针对目前主题网络爬虫搜索策略难以在全局范围内找到最优解,通过对遗传算法的分析与研究,文中设计了一个基于遗传算法的主题爬虫方案。引入了结合文本内容的PageRank算法;采用向量空间模型算法计算网页主题相关度;采取网页链接结构与主题相关度来评判网页的重要性;依据网页重要性选择爬行中的遗传因子;设置适应度函数筛选与主题相关的网页。与普通的主题爬虫比较,该策略能够获取大量主题相关度高的网页信息,能够提高获取的网页的重要性,能够满足用户对所需主题网页的检索需求,并在一定程度上解决了上述问题。     

Focussed crawling of environmental Web resources based on the combination of multimedia evidence

Focussed crawlers enable the automatic discovery of Web resources about a given topic by automatically navigating the Web link structure and selecting the hyperlinks to follow by estimating their relevance to the topic based on evidence obtained from the already downloaded pages. This work proposes a classifier-guided focussed crawling approach that estimates the relevance of a hyperlink to an unvisited Web resource based on the combination of textual evidence representing its local context, namely the textual content appearing in its vicinity in the parent page, with visual evidence associated with its global context, namely the presence of images relevant to the topic within the parent page. The proposed focussed crawling approach is applied towards the discovery of environmental Web resources that provide air quality measurements and forecasts, since such measurements (and particularly the forecasts) are not only provided in textual form, but are also commonly encoded as multimedia, mainly in the form of heatmaps. Our evaluation experiments indicate the effectiveness of incorporating visual evidence in the link selection process applied by the focussed crawler over the use of textual features alone, particularly in conjunction with hyperlink exploration strategies that allow for the discovery of highly relevant pages that lie behind apparently irrelevant ones.     

网络爬虫性能研究

受到学习模型爬虫的启发,主题爬虫结合网页内容和链接信息来估计网页对给定主题的相关性,得到两个新型的爬虫变种。新型爬虫强调的不仅是有学习相关网页内容的能力,而且有引向相关网页的能力,并且在查找特定主题方面的能力有质的提高。     

Inherit/Feedback:一种新的Web主题挖掘方法

经典链接分析方法(如PageRank和HITS)更多地关注的是网页的权威度，而不是其主题相关度，所以在引导主题搜索的过程中，很快就发生主题漂移．为此，在构建主题关联拓扑模型的基础上，提出了Inherit／Feedback方法，以用于Web主题挖掘．基本思想是：在搜索路径上，一个结点继承其父辈结点的主题相关度，并且将其主题相关度反馈给父辈结点．同时，提出了基于Inhefit／feedback的主题搜索算法(IFC)．实验结果表明，这种方法能有效地引导主题搜索，适用于对领域型网站做深层次的搜索和挖掘．     

面向主题爬取的多粒度URLs优先级计算方法

垂直检索系统中主题爬虫的性能对整个系统至关重要。在设计主题爬虫时需要解决两个问题一是计算当前页面与给定主题的相关度, 二是计算待爬取URLs的访问优先级。对第一个问题,给出利用页面的主题文本块和相关链接块的相关度计算方法; 对第二个问题, 给出基于主题上下文和四种不同的粒度(即站点级、页面级、块级和链接级)的优先级计算方法。在此基础上, 提出基于上述方法的主题爬取算法。实验证明, 新算法在不增加时间复杂度的前提下, 在查准率和信息量总和方面明显优于其他三种经典的爬取算法。     

An architecture for a focused trend parallel Web crawler with the application of clickstream analysis

The tremendous growth of the Web poses many challenges for all-purpose single-process crawlers including the presence of some irrelevant answers among search results and the coverage and scaling issues regarding the enormous dimension of the World Wide Web. Hence, more enhanced and convincing algorithms are on demand to yield more precise and relevant search results in an appropriate amount of time. Since employing link based Web page importance metrics within a multi-processes crawler bears a considerable communication overhead on the overall system and cannot produce the precise answer set, employing these metrics in search engines is not an absolute solution to identify the best search answer set by the overall search system. Thus considering the employment of a link independent Web page importance metric is required to govern the priority rule within the queue of fetched URLs. The aim of this paper is to propose a modest weighted architecture for a focused structured parallel Web crawler which employs a link independent clickstream based Web page importance metric. The experiments of this metric over the restricted boundary Web zone of our crowded UTM University Web site shows the efficiency of the proposed metric.     

改进的PageRank在Web信息搜集中的应用

PageRank是一种用于网页排序的算法,它利用网页间的相互引用关系评价网页的重要性·但由于它对每条出链赋予相同的权值,忽略了网页与主题的相关性,容易造成主题漂移现象·在分析了几种PageRank算法基础上,提出了一种新的基于主题分块的PageRank算法·该算法按照网页结构对网页进行分块,依照各块与主题的相关性大小对块中的链接传递不同的PageRank值,并能根据已访问的链接对块进行相关性反馈·实验表明,所提出的算法能较好地改进搜索结果的精确度·     

Exploiting interclass rules for focused crawling

Crawling the Web quickly and entirely is an expensive, unrealistic goal because of the required hardware and network resources. We started with a focused-crawling approach designed by Soumen Chakrabarti, Martin van den Berg, and Byron Dom, and we implemented the underlying philosophy of their approach to derive our baseline crawler. This crawler employs a canonical topic taxonomy to train a naive-Bayesian classifier, which then helps determine the relevancy of crawled pages. The crawler also relies on the assumption of topical locality to decide which URLs to visit next. Building on this crawler, we developed a rule-based crawler, which uses simple rules derived from interclass (topic) linkage patterns to decide its next move. This rule-based crawler also enhances the baseline crawler by supporting tunneling. A focused crawler gathers relevant Web pages on a particular topic. This rule-based Web-crawling approach uses linkage statistics among topics to improve a baseline focused crawler's harvest rate and coverage.     

领域相关的Web网站抓取方法

本文提出了一种抓取领域相关的Web站点的方法,可以在较小的代价下准确地收集用户所关心领域内的网站。这种方法主要改进了传统的聚焦爬虫（Focused Crawler）技术,首先利用Meta-Search技术来改进传统Crawler的通过链接分析来抓取网页的方法,而后利用启发式搜索大大降低了搜索代价,通过引入一种评价领域相关性的打分方法,迭到了较好的准确率。本文详细地描述了上述算法并通过详细的实验验证了算法的效率和效果。     

专题型网页搜集系统的设计与实现

近年来人们提出了很多新的搜集思想，他们都使用了一个共同的技术——集中式搜集。集中式搜集通过分析搜索的区域，来发现与主题最相关的链接，防止访问网上不相关的区域，这可以大量地节省硬件和网络资源，使网页得到尽快的更新。为了达到这个搜索目标，本文提出了两个算法：一个是基于多层分类的网页过滤算法，试验结果表明，这种算法有较高的准确率，而且分类速度明显高于一般的分类算法；另一个是基于Web结构的URL排序算法，这个算法充分地利用了Web的结构特征和网页的分布特征。     

Novel approaches to crawling important pages early

Web crawlers are essential to many Web applications, such as Web search engines, Web archives, and Web directories, which maintain Web pages in their local repositories. In this paper, we study the problem of crawl scheduling that biases crawl ordering toward important pages. We propose a set of crawling algorithms for effective and efficient crawl ordering by prioritizing important pages with the well-known PageRank as the importance metric. In order to score URLs, the proposed algorithms utilize various features, including partial link structure, inter-host links, page titles, and topic relevance. We conduct a large-scale experiment using publicly available data sets to examine the effect of each feature on crawl ordering and evaluate the performance of many algorithms. The experimental results verify the efficacy of our schemes. In particular, compared with the representative RankMass crawler, the FPR-title-host algorithm reduces computational overhead by a factor as great as three in running time while improving effectiveness by 5?% in cumulative PageRank.     

An approach for selecting seed URLs of focused crawler based on user-interest ontology

Seed URLs selection for focused Web crawler intends to guide related and valuable information that meets a user's personal information requirement and provide more effective information retrieval. In this paper, we propose a seed URLs selection approach based on user-interest ontology. In order to enrich semantic query, we first intend to apply Formal Concept Analysis to construct user-interest concept lattice with user log profile. By using concept lattice merger, we construct the user-interest ontology which can describe the implicit concepts and relationships between them more appropriately for semantic representation and query match. On the other hand, we make full use of the user-interest ontology for extracting the user interest topic area and expanding user queries to receive the most related pages as seed URLs, which is an entrance of the focused crawler. In particular, we focus on how to refine the user topic area using the bipartite directed graph. The experiment proves that the user-interest ontology can be achieved effectively by merging concept lattices and that our proposed approach can select high quality seed URLs collection and improve the average precision of focused Web crawler.     

一个面向实时网页分类的主题特征提取算法

网页实时分类是聚焦爬虫需要解决的重要问题,现有主题特征提取方法多数是面向离线分类的,性能达不到应用要求。本文首先扩展了标签树表示模型DocView的节点类型,且将其作为加权的重要因素,然后提出一个面向实时网页分类的Web文本和文本集主题特征提取算法。实验结果表明,算法的准确率提高了31％,主题偏移度降低了1倍多,能够满足应用要求。同时,还提出了一个新的主题特征提取性能评价模型。     

聚焦爬行中网页爬行算法的改进

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思路和方法。该文针对聚焦爬虫这一研究热点,对现今聚焦爬虫的爬行方法(主要是网页分析算法和网页搜索策略)做了深入分析和对比,提出了一种改进的聚焦爬行算法。这种基于类间规则的聚焦爬行方法借助baseline聚焦爬虫的架构,应用朴素的贝叶斯分类器并利用主题团间链接的统计关系构造规则找到在一定链接距离内的"未来回报"页面,并通过实验对该算法的性能进行分析、评价,证明其对聚焦爬虫的爬行收获率和覆盖率有很好的改善。     

Tunneling enhanced by web page content block partition for focused crawling

The complexity of web information environments and multiple‐topic web pages are negative factors significantly affecting the performance of focused crawling. A highly relevant region in a web page may be obscured because of low overall relevance of that page. Segmenting the web pages into smaller units will significantly improve the performance. Conquering and traversing irrelevant page to reach a relevant one (tunneling) can improve the effectiveness of focused crawling by expanding its reach. This paper presents a heuristic‐based method to enhance focused crawling performance. The method uses a Document Object Model (DOM)‐based page partition algorithm to segment a web page into content blocks with a hierarchical structure and investigates how to take advantage of block‐level evidence to enhance focused crawling by tunneling. Page segmentation can transform an uninteresting multi‐topic web page into several single topic context blocks and some of which may be interesting. Accordingly, focused crawler can pursue the interesting content blocks to retrieve the relevant pages. Experimental results indicate that this approach outperforms Breadth‐First, Best‐First and Link‐context algorithm both in harvest rate, target recall and target length. Copyright © 2007 John Wiley & Sons, Ltd.     

一种新的面向主题的爬行算法*

虽然通用网络爬行器已经给人们提供了极大的便利,但由于它的综合性不具备面向专业的特点,在准确性和速度等方面存在不足;面向主题的爬行器能弥补这些不足。主要研究面向主题网络爬行器两个方面的问题,即如何充分地定义主题和有效地排序爬行器待下载链接队列中的链接,使得只需访问很少的不相关页面就能够得到很多相关的页面链接。结合网页的半结构化信息特征,提出了一种新的基于内容的爬行策略,实验结果显示是一种寻找主题相关页面很有效的方法。