BibTeX
@MISC{Balakrishnan_graduatesupervisory,
author = {Raju Balakrishnan and Yi Chen and Anhai Doan and Huan Liu},
title = {Graduate Supervisory Committee:},
year = {}
}
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Abstract
Ranking is of definitive importance to both usability and profitability of web information systems. While ranking of results is crucial for the accessibility of information to the user, the ranking of online ads increases the profitability of the search provider. The scope of my thesis includes both search and ad ranking. I consider the emerging problem of ranking the deep web data considering trustworthiness and relevance. I address the end-to-end deep web ranking by focusing on: (i) ranking and selection of the deep web databases (ii) topic sensitive ranking of the sources (iii) ranking the result tuples from the selected databases. Especially, assessing the trustworthiness and relevances of results for ranking is hard since the currently used link analysis is inapplicable (since deep web records do not have links). I formulated a method—namely SourceRank—to assess the trustworthiness and relevance of the sources based on the inter-source agreement. Secondly, I extend the SourceRank to consider
