This paper concerns learning information by reading natural language texts. The major aim is to develop representations that are understandable by a reasoning engine and can be used to answer questions. We use abduction to map natural language sentences into concise and specific underlying theories. Techniques for automatically generating usable datarepresentations are discussed. New techniques are proposed to obtain semantically correct and precise logical representations from natural language, in particular in cases where its syntactic complexity results in fragmented logical forms.