Name
Abstract | ||
|---|---|---|
The classification of deep Web sources is an important area in large-scale deep Web integration, which is still at an early stage. Many deep web sources are structured by providing structured query interfaces and results. Classifying such structured sources into domains is one of the critical steps toward the integration of heterogeneous Web sources. To date, in terms of the classification, existing works mainly focus on classifying texts or Web documents, and there is little in the deep web. In this paper, we present a deep Web model and machine learning based classifying model. The experimental results show that we can achieve a good performance with a small scale training samples for each domain, and as the number of training samples increases, the performance keeps stabilization. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/FSKD.2007.54 | FSKD (4) |
Keywords | Field | DocType |
deep web sources classification,large-scale deep web integration,learning (artificial intelligence),deep web model,deep web source,classifying model,deep web,internet,structured source,machine learning approach classification,classifying text,web document,classification,machine learning,heterogeneous web source,deep web sources,learning artificial intelligence | Web mining,Information retrieval,Semantic Web Stack,Web mapping,Computer science,Web standards,Data Web,Web query classification,Web modeling,Artificial intelligence,Web navigation,Machine learning | Conference |
Volume | ISBN | Citations |
4 | 978-0-7695-2874-8 | 1 |
PageRank | References | Authors |
0.35 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hexiang Xu | 1 | 17 | 2.69 |
| Chenghong Zhang | 2 | 116 | 18.03 |
| Xiulan Hao | 3 | 22 | 3.91 |
| Yunfa Hu | 4 | 74 | 13.44 |