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Learning Object Models from Semistructured Web Documents
March 2006 (vol. 18 no. 3)
pp. 334-349
This paper presents an automated approach to learning object models by means of useful object data extracted from data-intensive semistructured web documents such as product descriptions. Modeling intensive data on the Web involves the following three phrases: First, we identify the object region covering the descriptions of object data when irrelevant contents from the web documents are excluded. Second, we partition the contents of different object data appearing in the object region and construct object data using hierarchical XML outputs. Third, we induce the abstract object model from the analogous object data. This model will match the corresponding object data from a Web site more precisely and comprehensively than the existing handcrafted ontologies. The main contribution of this study is in developing a fully automated approach to extract object data and object model from semistructured web documents using kernel-based matching and View Syntax interpretation. Our system, OnModer, can automatically construct object data and induce object models from complicated web documents, such as the technical descriptions of personal computers and digital cameras downloaded from manufacturers' and vendors' sites. A comparison with the available hand-crafted ontologies and tests on an open corpus demonstrate that our framework is effective in extracting meaningful and comprehensive models.

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Index Terms:
Index Terms- Web mining, machine learning, intelligent web services and Semantic Web, web text analysis, knowledge acquisition, ontology design, computational geometry and object modeling, DOM.
Citation:
Shiren Ye, Tat-Seng Chua, "Learning Object Models from Semistructured Web Documents," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 3, pp. 334-349, March 2006, doi:10.1109/TKDE.2006.47
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