Detecting Informative Web Page Blocks for Efficient Information Extraction Using Visual Block Segmentation

Shijun, Zhao; Zhang, Jinlei; XunLi,; Song, Wei

doi:10.1109/isitc.2007.6

Cited by 10 publications

(14 citation statements)

References 21 publications

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“…Typical information extraction tasks focus on data regions and data records. That implies that as the complexity of typical web documents increases, information extractors have to analyze more and more irrelevant regions, which has an impact on both efficiency and effectiveness [84], [163], [175]. This has motivated a number of authors to work on region extractors as a means to relieve information extractors from the burden of analyzing many regions of a web document that do not contain any relevant information [19], [23], [24], [53], [84], [97], [100], [114], [125], [141], [163], [169], [179], [180].…”

Section: Introductionmentioning

confidence: 99%

“…That implies that as the complexity of typical web documents increases, information extractors have to analyze more and more irrelevant regions, which has an impact on both efficiency and effectiveness [84], [163], [175]. This has motivated a number of authors to work on region extractors as a means to relieve information extractors from the burden of analyzing many regions of a web document that do not contain any relevant information [19], [23], [24], [53], [84], [97], [100], [114], [125], [141], [163], [169], [179], [180]. The difference between information extractors and region extractors is that the former focus on extracting and structuring data records and their attributes, whereas the latter focus on identifying the HTML fragments that contain this information.…”

Section: Introductionmentioning

confidence: 99%

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A Survey on Region Extractors from Web Documents

Sleiman

Corchuelo

2013

IEEE Trans. Knowl. Data Eng.

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Extracting information from web documents has become a research area in which new proposals sprout out year after year. This has motivated several researchers to work on surveys that attempt to provide an overall picture of the many existing proposals. Unfortunately, none of these surveys provide a complete picture, because they do not take region extractors into account. These tools are kind of preprocessors, because they help information extractors focus on the regions of a web document that contain relevant information. With the increasing complexity of web documents, region extractors are becoming a must to extract information from many websites. Beyond information extraction, region extractors have also found their way into information retrieval, focused web crawling, topic distillation, adaptive content delivery, mashups, and metasearch engines. In this paper, we survey the existing proposals regarding region extractors and compare them side by side.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Survey on Region Extractors from Web Documents

Sleiman

Corchuelo

2013

IEEE Trans. Knowl. Data Eng.

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“…Further, ND leads to website summarization. Jinbeom Kang et al [10] proposed the RIPB(Recognizing Informative Page Blocks) algorithm that detects the informative blocks in a Web page by exploiting the visual block segmentation scheme. RIPB uses the visual page segmentation algorithm to analyze and partition a Web page into a set of logical blocks, and then groups related blocks with similar structures into a block cluster and recognizes the informative block clusters by applying some heuristic rules to the cluster information.…”

Section: Related Workmentioning

confidence: 99%

Noise Reduction and Content Retrieval from Web Pages

Lingwal¹

2013

IJCA

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The World Wide Web is the most increasingly growing and accessible source of information. Web contents of different fields which can offer important information to users are available in the Web like multimedia data, structured, semistructured and unstructured data. But only a part of the information is useful for a particular application and the remaining information are considered as noises. Data on web pages contain formatting code, advertisement, navigation links, etc. This collection of unwanted noise with the real content in a web page complicates the task of automatic information extraction and processing. This requires the extraction of useful noise-free information. Otherwise, it can ruin the effectiveness of Web mining techniques. This paper proposes a novel method to filter web pages and retrieve the actual content of a web page. This research work proposed an approach for removing the noises from a given web page which will improve the performance of web content mining. At first, the web page information is divided into various blocks which then tokenized to separate the informative content from noises. This paper presents algorithm for removing noises from the web page and automatically extract important web content. This paper also presents the algorithm for global noise removal.

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“…The blocks making up the page are divided by the HTML tags (i.e. <table><div> [7] ). In this paper, all the blocks we talk about are blocks divided by the HTML Tags.…”

Section: Block-level Linksmentioning

confidence: 99%

Block-Level Linkes Based Content Extraction

Shen

Zhang

2011

2011 Fourth International Symposium on Parallel Architectures, Algorithms and Programming

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We present block-level links based content extraction (BLCE)-a method to extract content from the web pages by using the link attributes of blocks, which contains the number of links and the length of link text (anchor text).We describe how to divide one webpage into blocks and how to merge the similar blocks into one, then compute the number of links and the total length of anchor text. We find that extracting content only with the number of links and length of anchor text is not effective because the number of links and length of link text are proportional to the length of page. Density of links is a good method to solve this. So we use the content links ratios and the content anchor text ratios to describe the link attribute of the blocks. BLCE performs better than other methods especially in the new web pages with DIV and CSS where traditional algorithm can't work well.

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Detecting Informative Web Page Blocks for Efficient Information Extraction Using Visual Block Segmentation

Cited by 10 publications

References 21 publications

A Survey on Region Extractors from Web Documents

A Survey on Region Extractors from Web Documents

Noise Reduction and Content Retrieval from Web Pages

Block-Level Linkes Based Content Extraction

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