article · 2012

Deep Web Data Source Classification Based on Query Interface Context

Cited by 4Abstract and details only

Abstract

As the volume of information in the Deep Web grows, a Deep Web data source classification algorithm based on query interface context is presented. Two methods are combined to get the search interface similarity. One is based on the vector space. The classical TF-IDF statistics are used to gain the similarity between search interfaces. The other is to compute the two pages semantic similarity by the use of HowNet. Based on the K-NN algorithm, a WDB classification algorithm is presented. Experimental results show this algorithm generates high-quality clusters, measured both in terms of entropy and F-measure. It indicates the practical value of application.

Cite this paper

APA 7

Cui, Z., & Fu, Y. (2012). Deep Web Data Source Classification Based on Query Interface Context. https://doi.org/10.1109/iccis.2012.117

MLA 9

Cui, Zilu, and Yuchen Fu. “Deep Web Data Source Classification Based on Query Interface Context.” vol. 21, 2012, pp. 329–32, https://doi.org/10.1109/iccis.2012.117.

Chicago

Cui, Zilu, and Yuchen Fu. 2012. “Deep Web Data Source Classification Based on Query Interface Context.” https://doi.org/10.1109/iccis.2012.117.

Harvard

Cui, Z. and Fu, Y. (2012) ‘Deep Web Data Source Classification Based on Query Interface Context’. Available at: https://doi.org/10.1109/iccis.2012.117.

BibTeX

@article{cui2012deep,
  title = {{Deep Web Data Source Classification Based on Query Interface Context}},
  author = {Cui, Zilu and Fu, Yuchen},
  volume = {21},
  pages = {329--332},
  year = {2012},
  doi = {10.1109/iccis.2012.117}
}

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