Extracting structured data from Web pages

Arasu, Arvind; García-Molina, Héctor; University, Stanford

doi:10.1145/872797.872799

Cited by 204 publications

(344 citation statements)

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“…The simple tree match algorithm returns the number of these maximum matched pairs. The highlighted part in T ree 1 in Figure 4 shows the matched nodes for T ree 1 with respect to T ree 2 in Figure 4. The highlighted nodes indicate a match.…”

Section: Sibling Table Recognitionmentioning

confidence: 99%

“…The highlighted nodes are matched nodes in T ree 1 . Comparing the template match percentage for this mapped result for the three pattern templates in Figure 5, we obtain 93.3%, 53.3%, and 80% respectively.…”

Section: Pattern Generationmentioning

confidence: 99%

“…Much of this online information, indeed, the vast majority, is stored in databases on the so-called hidden web. 1 Hidden-web information is usually only accessible to users through search forms and is typically presented to them in tables. Automatically understanding hidden-web pages is a challenging task.…”

Section: Introductionmentioning

confidence: 99%

“…Tables may be nested Supported in part by the National Science Foundation under Grant #0414644. 1 There are more than 500 billion hidden-web pages. The surface web, which is indexed by common search engines only constitutes less than 1% of the World Wide Web.…”

Section: Introductionmentioning

confidence: 99%

“…It discovers data fields by string mismatches and discovers iterators and optionals by tag mismatches. EXALG [1] uses equivalence classes (sets of items that occur with the same frequency in sibling pages) and differentiating roles to generate extraction templates for the sibling pages. DEPTA [18] compares different records in a page instead of sibling pages and tries to find the extraction template for the record.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Hidden-Web Table Interpretation by Sibling Page Comparison

Tao¹,

Embley²

2007

Conceptual Modeling - ER 2007

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Abstract. The longstanding problem of automatic table interpretation still illudes us. Its solution would not only be an aid to table processing applications such as large volume table conversion, but would also be an aid in solving related problems such as information extraction and semi-structured data management. In this paper, we offer a conceptual modeling solution for the common special case in which so-called sibling pages are available. The sibling pages we consider are pages on the hidden web, commonly generated from underlying databases. We compare them to identify and connect nonvarying components (category labels) and varying components (data values). We tested our solution using more than 2,000 tables in source pages from three different domains-car advertisements, molecular biology, and geopolitical information. Experimental results show that the system can successfully identify sibling tables, generate structure patterns, interpret tables using the generated patterns, and automatically adjust the structure patterns, if necessary, as it processes a sequence of hidden-web pages. For these activities, the system was able to achieve an overall F-measure of 94.5%.

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Section: Sibling Table Recognitionmentioning

confidence: 99%

Section: Pattern Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic Hidden-Web Table Interpretation by Sibling Page Comparison

Tao¹,

Embley²

2007

Conceptual Modeling - ER 2007

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Automatic review identification on the web using pattern recognition

Meer

Frăsincar

2012

Softw Pract Exp

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SUMMARYIn this paper we propose the Automatic Review Recognition and annOtation of Webpages method, which is capable of identifying and annotating user submitted reviews on a Web page. The method consists of six steps: data preparation, page‐level pattern identification, subjectivity analysis, container structure analysis, review properties identification, and review annotation. For the evaluation, we have implemented the method and tested it on various review websites. On the basis of the performed evaluation, we conclude that our method is capable of identifying and annotating the majority of reviews. Copyright © 2012 John Wiley & Sons, Ltd.

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