NDLI: Combining topic models and string kernel for deep web categorization

Content Provider	IEEE Xplore Digital Library
Author	Guangyue Xu Weimin Zheng Haiping Wu Yujiu Yang
Copyright Year	2010
Description	Author affiliation: Division of Informatics, Graduate School at Shenzhen, Tsinghua University, China (Haiping Wu; Yujiu Yang) \|\| Department of Computer Science and Technology, Tsinghua University, Beijing, China (Guangyue Xu; Weimin Zheng)
Abstract	Online databases maintain a collection of structured domain-specific documents dynamically generated in response to users' queries instead of being accessed by static URLs. Categorizing deep webs according to their object domains is a critical step to integrate such sources. While existing methods focus on supervised or post-query methodologies, we propose a more practical pre-query algorithm operating in an unsupervised manner. Given the domain number, our two phase approach firstly investigates the hidden domain distribution for each query form using topic models and each query form's object domain can be identified preliminarily. In this phase, we construct our training set composing the query forms deemed to have already been categorized correctly, and beside, the deep webs needed to be reclassified are also selected in this phase. In the second phase, we train a classifier with String Kernel methods to reclassify the uncertain deep webs to improve the overall performance. The advantage of our algorithm over previous ones is that we capture the semantic structure for each query form. Based on the two phase architecture, our framework works in an unsupervised manner and achieves satisfactory results. Experiments on the TEL-8 dataset from the UIUC Web integration $repository^{1}$ show the effectiveness and efficiency of our algorithm.
Starting Page	2791
Ending Page	2795
File Size	328721
Page Count	5
File Format	PDF
ISBN	9781424459315
e-ISBN	9781424459346
DOI	10.1109/FSKD.2010.5569236
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Frequency modulation Databases Atmospheric modeling Semantics Books Kernel
Content Type	Text
Resource Type	Article

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