Ontology-Based Deep Web Data Sources Selection

Authors:
Wei Fang;Pengyu Hu;Pengpeng Zhao;Zhiming Cui
Affiliations:
Jiangsu Key Laboratory of Computer Information Processing Technology, Soochow University, Jiangsu Suzhou, China;Jiangsu Key Laboratory of Computer Information Processing Technology, Soochow University, Jiangsu Suzhou, China;Jiangsu Key Laboratory of Computer Information Processing Technology, Soochow University, Jiangsu Suzhou, China;Jiangsu Key Laboratory of Computer Information Processing Technology, Soochow University, Jiangsu Suzhou, China
Venue:
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Year:
2008

Citing 6
Cited 0

Databases Deepen the Web

Computer
When one sample is not enough: improving text database selection using shrinkage

SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Query Selection Techniques for Efficient Crawling of Structured Web Sources

ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Optimizing web search using social annotations

Proceedings of the 16th international conference on World Wide Web
Automatic Generation of Ontology from the Deep Web

DEXA '07 Proceedings of the 18th International Conference on Database and Expert Systems Applications
Ontology-based focused crawling of deep web sources

KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management

Quantified Score

Hi-index	0.00

Visualization

Abstract

Many Web applications are all required high quality information from the Deep Web data sources at present. Such as the Deep Web data source discovering, the extract from query results, and the information integration of query results. They are the key technology in the process of Deep Web information integration. This paper proposes a data sources selection method based on domain ontology. It is used for finding the most relevant sets of query interface related to domain ontology, which is a premise for Deep Web information integration. This approach is based on the interface for its own features and the back-end database semantic. Therefore, it makes better performance than existing approaches.