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xCrawl: a high-recall crawling method for Web mining

机译：xCrawl：一种用于Web挖掘的高调用爬网方法

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摘要

Web mining systems exploit the redundancy of data published on the Web to automatically extract information from existing Web documents. The first step in the Information Extraction process is thus to locate as many Web pages as possible that contain relevant information within a limited period of time, a task which is commonly accomplished by applying focused crawling techniques. The performance of such a crawler can be measured by its “recall”, i.e., the percentage of documents found and identified as relevant compared to the total number of existing documents. A higher recall value implies that more redundant data are available, which in turn leads to better results in the subsequent fact extraction phase of the Web mining process. In this paper, we propose xCrawl, a new focused crawling method which outperforms state-of-the-art approaches with respect to the recall values achievable within a given period of time. This method is based on a new combination of ideas and techniques used to identify and exploit the navigational structures of Web sites, such as hierarchies, lists, or maps. In addition, automatic query generation is applied to rapidly collect Web sources containing target documents. The proposed crawling technique was inspired by the requirements of a Web mining system developed to extract product and service descriptions given in tabular form and was evaluated in different application scenarios. Comparisons with existing focused crawling techniques reveal that the new crawling method leads to a significant increase in recall while maintaining precision.
机译：Web挖掘系统利用Web上发布的数据的冗余来自动从现有Web文档中提取信息。因此，信息提取过程的第一步是在有限的时间内找到尽可能多的包含相关信息的网页，这通常是通过应用集中爬网技术来完成的。这种履带的性能可以通过其“召回率”来衡量，即与现有文件总数相比，找到并确定为相关文件的百分比。较高的召回值意味着有更多的冗余数据可用，从而在Web挖掘过程的后续事实提取阶段中带来了更好的结果。在本文中，我们提出了xCrawl，这是一种新的集中式爬网方法，在给定时间内可以实现的召回值方面，它优于最新方法。此方法基于用于识别和利用网站的导航结构（例如层次结构，列表或地图）的思想和技术的新组合。此外，自动查询生成可用于快速收集包含目标文档的Web源。 Web挖掘系统的需求启发了提出的爬网技术，该系统的开发目的是提取以表格形式给出的产品和服务描述，并在不同的应用场景中对其进行评估。与现有的集中爬网技术的比较表明，新的爬网方法可在保持精度的同时显着提高召回率。

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