Focused crawling is an important technique for topical resource discovery on the Web.The key issue in focused crawling is to prioritize uncrawled uniform resource locators(URLs) in the frontier to focus the crawling o...Focused crawling is an important technique for topical resource discovery on the Web.The key issue in focused crawling is to prioritize uncrawled uniform resource locators(URLs) in the frontier to focus the crawling on relevant pages.Traditional focused crawlers mainly rely on content analysis.Link-based techniques are not effectively exploited despite their usefulness.In this paper,we propose a new frontier prioritizing algorithm,namely the on-line topical importance estimation(OTIE) algorithm.OTIE combines link-and content-based analysis to evaluate the priority of an uncrawled URL in the frontier.We performed real crawling experiments over 30 topics selected from the Open Directory Project(ODP) and compared harvest rate and target recall of the four crawling algorithms:breadth-first,link-context-prediction,on-line page importance computation(OPIC) and our OTIE.Experimental results showed that OTIE significantly outperforms the other three algorithms on the average target recall while maintaining an acceptable harvest rate.Moreover,OTIE is much faster than the traditional focused crawling algorithm.展开更多
目前主流开源爬虫框架在分析页面与主题领域关联性上,常采用基于关键词的量化和向量空间模型算法相融合,但融合疏忽了界面语义与特定主题间的关联,导致爬取内容与主题产生偏差。为了给金融等领域的舆情分析提供准确的数据支撑,提出一种...目前主流开源爬虫框架在分析页面与主题领域关联性上,常采用基于关键词的量化和向量空间模型算法相融合,但融合疏忽了界面语义与特定主题间的关联,导致爬取内容与主题产生偏差。为了给金融等领域的舆情分析提供准确的数据支撑,提出一种面向领域扩展主题库的爬虫及系统,通过扩展主题特征库,融合向量空间模型(Vector Space Model,VSM)与超链接主题搜索算法(Hyperlink-Induced Topic Search,HITS),优化了主题页面相关度计算,并针对股票舆情信息爬取进行仿真。结果表明,上述扩展主题型爬虫在爬取准确率和效率等方面有较好地提升,能够有效地完成领域主题信息的爬取任务。展开更多
基金Project (No.2007C23086) supported by the Science and Technology Plan of Zhejiang Province,China
文摘Focused crawling is an important technique for topical resource discovery on the Web.The key issue in focused crawling is to prioritize uncrawled uniform resource locators(URLs) in the frontier to focus the crawling on relevant pages.Traditional focused crawlers mainly rely on content analysis.Link-based techniques are not effectively exploited despite their usefulness.In this paper,we propose a new frontier prioritizing algorithm,namely the on-line topical importance estimation(OTIE) algorithm.OTIE combines link-and content-based analysis to evaluate the priority of an uncrawled URL in the frontier.We performed real crawling experiments over 30 topics selected from the Open Directory Project(ODP) and compared harvest rate and target recall of the four crawling algorithms:breadth-first,link-context-prediction,on-line page importance computation(OPIC) and our OTIE.Experimental results showed that OTIE significantly outperforms the other three algorithms on the average target recall while maintaining an acceptable harvest rate.Moreover,OTIE is much faster than the traditional focused crawling algorithm.
文摘目前主流开源爬虫框架在分析页面与主题领域关联性上,常采用基于关键词的量化和向量空间模型算法相融合,但融合疏忽了界面语义与特定主题间的关联,导致爬取内容与主题产生偏差。为了给金融等领域的舆情分析提供准确的数据支撑,提出一种面向领域扩展主题库的爬虫及系统,通过扩展主题特征库,融合向量空间模型(Vector Space Model,VSM)与超链接主题搜索算法(Hyperlink-Induced Topic Search,HITS),优化了主题页面相关度计算,并针对股票舆情信息爬取进行仿真。结果表明,上述扩展主题型爬虫在爬取准确率和效率等方面有较好地提升,能够有效地完成领域主题信息的爬取任务。
