Learning to Surface Deep Web Content
DOI:
https://doi.org/10.1609/aaai.v24i1.7779Keywords:
hidden web, deep web crawling, reinforcement learningAbstract
We propose a novel deep web crawling framework based on reinforcement learning. The crawler is regarded as an agent and deep web database as the environment. The agent perceives its current state and submits a selected action (query) to the environment according to Q-value. Based on the framework we develop an adaptive crawling method. Experimental results show that it outperforms the state of art methods in crawling capability and breaks through the assumption of full-text search implied by existing methods.
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Published
2010-07-05
How to Cite
Wu, Z., Jiang, L., Zheng, Q., & Liu, J. (2010). Learning to Surface Deep Web Content. Proceedings of the AAAI Conference on Artificial Intelligence, 24(1), 1967-1968. https://doi.org/10.1609/aaai.v24i1.7779
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Student Abstracts