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Probabilistic Reasoning about Human Edits in Information Integration

We present a probabilistic framework for knowledge base (KB) management that allows users to correct data integration errors in the KB. Rather than allowing users to directly edit the KB, we instead treat user edits as assertions on the truth about the entities and relations in the KB. Probabilistic inference then considers the user assertions along with all other available evidence in order to predict the true data values in the KB (and jointly infer the reliabilities of the contributing users). We evaluate our approach on a bibliographic database of computer science authors and show that our system is more eective at correcting entity attribute and coreference errors than traditional approaches that grant users the ability to directly edit the database.