The KNEXT system produces a large volume of factoids from text, expressing possibilistic general claims such as that 'A PERSON MAY HAVE A HEAD' or 'PEOPLE MAY SAY SOMETHING'. We present a rule-based method to sharpen certain classes of factoids into stronger, quantified claims such as 'ALL OR MOST PERSONS HAVE A HEAD' or 'ALL OR MOST PERSONS AT LEAST OCCASIONALLY SAY SOMETHING' - statements strong enough to be used for inference. The judgement of whether and how to sharpen a factoid depends on the semantic categories of the terms involved and the strength of the quantifier depends on how strongly the subject is associated with what is what is predicated of it. We provide an initial assessment of the quality of such automatic strengthening of knowledge and examples of reasoning with multiple sharpened premises.
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