Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Autonomously semantifying wikipedia
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Truth Discovery with Multiple Conflicting Information Providers on the Web
IEEE Transactions on Knowledge and Data Engineering
Truth discovery and copying detection in a dynamic world
Proceedings of the VLDB Endowment
Corroborating information from disagreeing views
Proceedings of the third ACM international conference on Web search and data mining
Knowing what to believe (when you already know something)
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Proceedings of the 20th international conference companion on World wide web
A Bayesian approach to discovering truth from conflicting sources for data integration
Proceedings of the VLDB Endowment
DeFacto - deep fact validation
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
Less is more: selecting sources wisely for integration
Proceedings of the VLDB Endowment
Truth finding on the deep web: is the problem solved?
Proceedings of the VLDB Endowment
Multi-source deep learning for information trustworthiness estimation
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Compact explanation of data fusion decisions
Proceedings of the 22nd international conference on World Wide Web
Proceedings of the 22nd international conference on World Wide Web
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Information retrieval may suggest a document, and information extraction may tell us what it says, but which information sources do we trust and which assertions do we believe when different authors make conflicting claims? Trust algorithms known as fact-finders attempt to answer these questions, but consider only which source makes which claim, ignoring a wealth of background knowledge and contextual detail such as the uncertainty in the information extraction of claims from documents, attributes of the sources, the degree of similarity among claims, and the degree of certainty expressed by the sources. We introduce a new, generalized fact-finding framework able to incorporate this additional information into the fact-finding process. Experiments using several state-of-theart fact-finding algorithms demonstrate that generalized fact-finders achieve significantly better performance than their original variants on both semi-synthetic and real-world problems.