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A decision tree framework for semi-automatic extraction of product attributes from the web

    Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

    Abstract

    Semi-Automatic extraction of product attributes from URLs is an important issue for comparison-shopping agents. In this paper we examine a novel decision tree framework for extracting product attributes. The core induction algorithmic framework consists of three main stages. In the first stage, a large set of regular expression-based patterns are induced by employing a longest common subsequence algorithm. In the second stage we filter the initial set and leave only the most useful patterns. In the last stage we represent the extraction problem (in which the domain values are not known in advance) as a classification problem and employ an ensemble of decision trees. An empirical study performed on a real-world extraction tasks illustrates the capability of the proposed framework.

    Original languageEnglish
    Title of host publicationAdvances in Web Intelligence and Data Mining
    EditorsMark Last, Piotr Szczepaniak, Piotr Szczepaniak, Zeev Vlvolkov, Abraham Kandel
    Pages201-210
    Number of pages10
    DOIs
    StatePublished - 27 Sep 2006

    Publication series

    NameStudies in Computational Intelligence
    Volume23
    ISSN (Print)1860-949X

    ASJC Scopus subject areas

    • Artificial Intelligence

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