Concept Hierarchy-Based Text Database Categorization

Meng, Weiyi; Wang, Wenxian; Sun, Hongyu; Yu, Clement

doi:10.1007/s101150200001

Cited by 29 publications

(36 citation statements)

References 19 publications

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“…Our approach is designed to address these issues while employing a different clustering method. ESE clustering is related to search engine categorization [9,13]. The search engines considered in these two papers are document search engines (which search documents/Web pages) not ESEs (which search structured product data).…”

Section: Introductionmentioning

confidence: 99%

Clustering e-commerce search engines based on their search interface pages using WISE-Cluster

Lu¹,

He²,

Peng³

et al. 2006

Data & Knowledge Engineering

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In this paper, we propose a new approach to clustering e-commerce search engines (ESEs) on the Web. Our approach utilizes the features available on the interface page of each ESE, including the label terms and value terms appearing in the search form, the number of images, normalized price terms as well as other terms. The experimental results based on more than 400 ESEs indicate that the proposed approach has good clustering accuracy. The importance of different types of features is analyzed and the terms in the search form are the most important feature in obtaining quality clusters.

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Section: Introductionmentioning

confidence: 99%

Clustering e-commerce search engines based on their search interface pages using WISE-Cluster

Lu¹,

He²,

Peng³

et al. 2006

Data & Knowledge Engineering

Self Cite

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“…More recently, Ipeirotis and Gravano (2004) introduce the notion of "shrinkage" which exploits database classification information by use of a topic hierarchy to compensate for incomplete content summaries. In Meng et al (2002) and Yu et al (1999b), a concept hierarchy is constructed for text database categorization. Each concept description is treated as a query that is submitted to the database.…”

Section: Introductionmentioning

confidence: 99%

Two-stage statistical language models for text database selection

Yang

Zhang

2006

Inf Retrieval

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As the number and diversity of distributed Web databases on the Internet exponentially increase, it is difficult for user to know which databases are appropriate to search. Given database language models that describe the content of each database, database selection services can provide assistance in locating databases relevant to the information needs of users. In this paper, we propose a database selection approach based on statistical language modeling. The basic idea behind the approach is that, for databases that are categorized into a topic hierarchy, individual language models are estimated at different search stages, and then the databases are ranked by the similarity to the query according to the estimated language model. Two-stage smoothed language models are presented to circumvent inaccuracy due to word sparseness. Experimental results demonstrate that such a language modeling approach is competitive with current state-of-the-art database selection approaches.

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“…ESE clustering is related to search engine categorization [1,2]. The methods described in [1,2] are query-submission based and they consider document search engines, not ESEs.…”

Section: Introductionmentioning

confidence: 99%

“…The methods described in [1,2] are query-submission based and they consider document search engines, not ESEs. In contrast, our method uses only the Web pages that contain the search forms of the considered ESEs to perform clustering (no queries are submitted to these ESEs).…”

Section: Introductionmentioning

confidence: 99%