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The Discovery of the Equator or Concept Driven Learning

This paper presents a model-driven method for machine learning of inference rules, which involves both: 'learning by induction' and 'learning by being told'. By the use of higher concepts (like transitivity and conversity) attributes of and relations among two-place predicates are discovered by induction. This new knowledge is represented as metafacts which can be transformed into inference rules if needed. The relations among meta facts are expressed as meta rules. The higher concept of support sets correspond to the domains for which meta facts are true. The process of restructuring support sets in order to resolve contradictions (and to make inference rules more precise) is discussed.