Clustering Visually Similar Web Page Elements for Structured Web Data Extraction [chapter]

Tomas Grigalis, Lukas Radvilavičius, Antanas Čenys, Juozas Gordevičius
<span title="">2012</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
We propose a novel approach for extraction of structured web data called ClustVX. It clusters visually similar web page elements by exploiting their visual formatting and structural features. Clusters are then used to derive extraction rules. The experimental evaluation results of ClustVX system on three publicly available benchmark data sets outperform state-of-the-art structured data extraction systems.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-31753-8_38">doi:10.1007/978-3-642-31753-8_38</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/v5inxjfvyfe6xghcwfkcouxaxa">fatcat:v5inxjfvyfe6xghcwfkcouxaxa</a> </span>
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