Most information extraction (IE) systems identify facts that are explicitly stated in text. However, in natural language, some facts are implicit, and identifying them requires ?read- ing between the lines?. Human readers nat- urally use common sense knowledge to in- fer such implicit information from the explic- itly stated facts. We propose an approach that uses Bayesian Logic Programs (BLPs), a statistical relational model combining first- order logic and Bayesian networks, to infer additional implicit information from extracted facts. It involves learning uncertain common- sense knowledge (in the form of probabilis- tic first-order rules) from natural language text by mining a large corpus of automatically ex- tracted facts. These rules are then used to de- rive additional facts from e...

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