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Visual Pattern Detection in Web Pages
Kotraš, Martin ; Bartík, Vladimír (referee) ; Burget, Radek (advisor)
The work solves the extraction of information from websites using the technique of searching for visual patterns - spatial relations between areas on the website and the same visual styles of these areas - with the extension of new techniques to improve results. It uses a user-specified ontological data model, which describes which data items will be extracted from the specified web page and how the individual items on the page look, mainly from a text point of view. As part of the work, a console application VizGet in Java was created using the FitLayout framework to obtain a visual model of the website. Testing the application on 7 different domains, including a list of the best movies, e-shop products, or weather forecasts, showed that the success rate of the application ranges in about 75 % of subtests above 85 % F-score and in more than 90 % of subtests above 60 % F-score, where 45 % of subtests achieve an F-score of 100 %. The VizGet application can thus be deployed for practical use in non-critical applications, while it is open to further extensions and possibilities for improvement.
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Visual Pattern Detection in Web Pages
Kotraš, Martin ; Bartík, Vladimír (referee) ; Burget, Radek (advisor)
The work solves the extraction of information from websites using the technique of searching for visual patterns - spatial relations between areas on the website and the same visual styles of these areas - with the extension of new techniques to improve results. It uses a user-specified ontological data model, which describes which data items will be extracted from the specified web page and how the individual items on the page look, mainly from a text point of view. As part of the work, a console application VizGet in Java was created using the FitLayout framework to obtain a visual model of the website. Testing the application on 7 different domains, including a list of the best movies, e-shop products, or weather forecasts, showed that the success rate of the application ranges in about 75 % of subtests above 85 % F-score and in more than 90 % of subtests above 60 % F-score, where 45 % of subtests achieve an F-score of 100 %. The VizGet application can thus be deployed for practical use in non-critical applications, while it is open to further extensions and possibilities for improvement.
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