Please use this identifier to cite or link to this item: https://doi.org/10.1109/TKDE.2006.47
Title: Learning object models from semistructured web documents
Authors: Ye, S. 
Chua, T.-S. 
Keywords: Computational geometry and object modeling
DOM
Intelligent web services and Semantic Web
Knowledge acquisition
Machine learning
Ontology design
Web mining
Web text analysis
Issue Date: 2006
Source: Ye, S.,Chua, T.-S. (2006). Learning object models from semistructured web documents. IEEE Transactions on Knowledge and Data Engineering 18 (3) : 334-349. ScholarBank@NUS Repository. https://doi.org/10.1109/TKDE.2006.47
Abstract: 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. © 2006 IEEE.
Source Title: IEEE Transactions on Knowledge and Data Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/39140
ISSN: 10414347
DOI: 10.1109/TKDE.2006.47
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

19
checked on Dec 13, 2017

WEB OF SCIENCETM
Citations

12
checked on Nov 4, 2017

Page view(s)

62
checked on Dec 16, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.