document: SourceRank : relevance and trust assessment for deep web sources based on inter-source agreement One immediate challenge in searching the deep web databases is source selection - i.e. selecting the most relevant web databases for answering a given query . The existing database selection methods ( both text and relational ) assess the source quality based on the query-similarity-based relevance assessment . When applied to the deep web these methods have two deficiencies . First is that the methods are agnostic to the correctness ( trustworthiness ) of the sources . Secondly , the query based relevance does not consider the importance of the results . These two considerations are essential for the open collections like the deep web . Since a number of sources provide answers to any query , we conjuncture that the agreements between these answers are likely to be helpful in assessing the importance and the trustworthiness of the sources . We compute the agreement between the sources as the agreement of the answers returned . While computing the agreement , we also measure and compensate for possible collusion between the sources . This adjusted agreement is modeled as a graph with sources at the vertices . On this agreement graph , a quality score of a source that we call SourceRank , is calculated as the stationary visit probability of a random walk . We evaluate SourceRank in multiple domains , including sources in Google Base , with sizes up to 675 sources . We demonstrate that the SourceRank tracks source corruption . Further , our relevance evaluations show that SourceRank improves precision by 22-60 % over the Google Base and the other baseline methods . SourceRank has been implemented in a system called Factal .
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