Searching distributed collections with inference networks
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
A Frequency-based Approach for Mining Coverage Statistics in Data Integration
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Truth Discovery with Multiple Conflicting Information Providers on the Web
IEEE Transactions on Knowledge and Data Engineering
Heterogeneous network-based trust analysis: a survey
ACM SIGKDD Explorations Newsletter
Assessing relevance and trust of the deep web sources and results based on inter-source agreement
ACM Transactions on the Web (TWEB)
Agreement based source selection for the multi-topic deep web integration
Proceedings of the 17th International Conference on Management of Data
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We consider the problem of deep web source selection and argue that existing source selection methods are inadequate as they are based on local similarity assessment. Specically, they fail to account for the fact that sources can vary in trustworthiness and individual results can vary in importance. In response, we formulate a global measure to calculate relevance and trustworthiness of a source based on agreement between the answers provided by different sources. Agreement is modeled as a graph with sources at the vertices. On this agreement graph, source quality scores - namely SourceRank - are calculated as the stationary visit probability of a weighted random walk. Our experiments on online databases and 675 book sources from Google Base show that SourceRank improves relevance of the results by 25-40% over existing methods and Google Base ranking. SourceRank also reduces linearly with the corruption levels of the sources.