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。     

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。     

Empirical evaluation of the link and content-based focused Treasure-Crawler

Indexing the Web is becoming a laborious task for search engines as the Web exponentially grows in size and distribution. Presently, the most effective known approach to overcome this problem is the use of focused crawlers. A focused crawler employs a significant and unique algorithm in order to detect the pages on the Web that relate to its topic of interest. For this purpose we proposed a custom method that uses specific HTML elements of a page to predict the topical focus of all the pages that have an unvisited link within the current page. These recognized on-topic pages have to be sorted later based on their relevance to the main topic of the crawler for further actual downloads. In the Treasure-Crawler, we use a hierarchical structure called T-Graph which is an exemplary guide to assign appropriate priority score to each unvisited link. These URLs will later be downloaded based on this priority. This paper embodies the implementation, test results and performance evaluation of the Treasure-Crawler system. The Treasure-Crawler is evaluated in terms of specific information retrieval criteria such as recall and precision, both with values close to 50%. Gaining such outcome asserts the significance of the proposed approach.     

一种专题Web信息采集系统的设计方案

飞速发展的网络给综合性的采集系统带来了巨大的挑战,由此小型的专题信息采集已成为近年的研究热点。文章介绍了专题的Web信息采集系统的基本原理,分析了专题页面在网络中的分布特性,提出了一种通过提供高质量种子集的方法来改善采集器性能的方法,节约了硬件和网络资源,使更新更加容易。     

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。     

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.     

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.     

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。     

A topic-specific crawling strategy based on semantics similarity

With the Internet growing exponentially, search engines are encountering unprecedented challenges. A focused search engine selectively seeks out web pages that are relevant to user topics. Determining the best strategy to utilize a focused search is a crucial and popular research topic. At present, the rank values of unvisited web pages are computed by considering the hyperlinks (as in the PageRank algorithm), a Vector Space Model and a combination of them, and not by considering the semantic relations between the user topic and unvisited web pages. In this paper, we propose a concept context graph to store the knowledge context based on the user's history of clicked web pages and to guide a focused crawler for the next crawling. The concept context graph provides a novel semantic ranking to guide the web crawler in order to retrieve highly relevant web pages on the user's topic. By computing the concept distance and concept similarity among the concepts of the concept context graph and by matching unvisited web pages with the concept context graph, we compute the rank values of the unvisited web pages to pick out the relevant hyperlinks. Additionally, we constitute the focused crawling system, and we retrieve the precision, recall, average harvest rate, and F-measure of our proposed approach, using Breadth First, Cosine Similarity, the Link Context Graph and the Relevancy Context Graph. The results show that our proposed method outperforms other methods.     

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。     

Discovering and Analyzing World Wide Web Collections

With the explosive growth of the World Wide Web, it is becoming increasingly difficult for users to discover Web pages that are relevant to a topic. To address this problem we are developing a system that allows the collection and analysis of Web pages related to a particular topic. In this paper we present the systems overall architecture and introduce the focused crawler used by the system. We also discuss the various techniques we use to allow the user to analyze and gain useful insights about a collection. Finally, we present some statistics on the collections.     

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.     

一种基于HITS的主题敏感爬行方法

基于主题的信息采集是信息检索领域内一个新兴且实用的方法,通过将下载页面限定在特定的主题领域,来提高搜索引擎的效率和提供信息的质量。其思想是在爬行过程中按预先定义好的主题有选择地收集相关网页,避免下载主题不相关的网页,其目标是更准确地找到对用户有用的信息。探讨了主题爬虫的一些关键问题,通过改进主题模型、链接分类模型的学习方法及链接分析方法来提高下载网页的主题相关度及质量。在此基础上设计并实现了一个主题爬虫系统,该系统利用主题敏感HITS来计算网页优先级。实验表明效果良好。     

一种基于HITS的主题敏感爬行方法

基于主题的信息采集是信息检索领域内一个新兴且实用的方法,通过将下载页面限定在特定的主题领域,来提高搜索引擎的效率和提供信息的质量。其思想是在爬行过程中按预先定义好的主题有选择地收集相关网页,避免下载主题不相关的网页,其目标是更准确地找到对用户有用的信息。探讨了主题爬虫的一些关键问题,通过改进主题模型、链接分类模型的学习方法及链接分析方法来提高下载网页的主题相关度及质量。在此基础上设计并实现了一个主题爬虫系统,该系统利用主题敏感HITS来计算网页优先级。实验表明效果良好。     

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.     

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。     

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。     

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.     

PROBABILISTIC MODELS FOR FOCUSED WEB CRAWLING

A focused crawler is an efficient tool used to traverse the Web to gather documents on a specific topic. It can be used to build domain‐specific Web search portals and online personalized search tools. Focused crawlers can only use information obtained from previously crawled pages to estimate the relevance of a newly seen URL. Therefore, good performance depends on powerful modeling of context as well as the quality of the current observations. To address this challenge, we propose capturing sequential patterns along paths leading to targets based on probabilistic models. We model the process of crawling by a walk along an underlying chain of hidden states, defined by hop distance from target pages, from which the actual topics of the documents are observed. When a new document is seen, prediction amounts to estimating the distance of this document from a target. Within this framework, we propose two probabilistic models for focused crawling, Maximum Entropy Markov Model (MEMM) and Linear‐chain Conditional Random Field (CRF). With MEMM, we exploit multiple overlapping features, such as anchor text, to represent useful context and form a chain of local classifier models. With CRF, a form of undirected graphical models, we focus on obtaining global optimal solutions along the sequences by taking advantage not only of text content, but also of linkage relations. We conclude with an experimental validation and comparison with focused crawling based on Best‐First Search (BFS), Hidden Markov Model (HMM), and Context‐graph Search (CGS).     

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.