Block-based web search
Block-based web search
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DOI：
10.1145/1008992.1009070
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发表时间：
2004-07
期刊：
影响因子：
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通讯作者：
Deng Cai;Shipeng Yu;Ji-Rong Wen;Wei-Ying Ma
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文献类型：
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作者：
Deng Cai;Shipeng Yu;Ji-Rong Wen;Wei-Ying Ma
网页的多主题性和变长性是影响网络搜索性能的两个重要因素。在本文中，我们将探讨使用页面分割算法划分网页成块，并研究如何利用块级证据，以提高检索性能的Web上下文。由于网页的特殊性，不同的网页分割方法对网页搜索性能的影响也不同。我们比较了四种类型的方法，包括固定长度的页面分割，基于DOM的页面分割，基于视觉的页面分割，并结合方法，它集成了语义和固定长度的属性。对块级查询扩展和检索进行了实验。在这四种方法中，结合的方法实现了最好的Web搜索性能。我们的实验结果也表明，这样的网页语义划分有效地处理多漂移的主题和混合长度的问题，从而有很大的潜力，以提高当前的Web搜索引擎的性能。
Multiple-topic and varying-length of web pages are two negative factors significantly affecting the performance of web search. In this paper, we explore the use of page segmentation algorithms to partition web pages into blocks and investigate how to take advantage of block-level evidence to improve retrieval performance in the web context. Because of the special characteristics of web pages, different page segmentation method will have different impact on web search performance. We compare four types of methods, including fixed-length page segmentation, DOM-based page segmentation, vision-based page segmentation, and a combined method which integrates both semantic and fixed-length properties. Experiments on block-level query expansion and retrieval are performed. Among the four approaches, the combined method achieves the best performance for web search. Our experimental results also show that such a semantic partitioning of web pages effectively deals with the problem of multiple drifting topics and mixed lengths, and thus has great potential to boost up the performance of current web search engines.