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<p>A method for learning knowledge from a database is used to address the bottleneck of manual knowledge acquisition. An attempt is made to improve representation with the assistance of experts and from computer resident knowledge. The knowledge representation is described in the framework of a conceptual schema consisting of a semantic model and an event model. A concept classifies a domain into different subdomains. As a method of knowledge acquisition, inductive learning techniques are used for rule generation. The theory of rough sets is used in designing the learning algorithm. Examples of certain concepts are used to induce general specifications of the concepts called classification rules. The basic approach is to partition the information into equivalence classes and to derive conclusions based on equivalence relations. In a sense, what is involved is a data-reduction process, where the goal is to reduce a large database of information to a small number of rules describing the domain. This completely integrated approach includes user interface, semantics, constraints, representations of temporal events, induction, etc.</p>
knowledge representation; classification rules; knowledge acquisition; database; computer resident knowledge; conceptual schema; semantic model; event model; inductive learning techniques; rule generation; rough sets; classification rules; equivalence classes; data-reduction process; user interface; semantics; temporal events; induction; classification; knowledge acquisition; knowledge representation; learning systems

R. Yasdi, "Learning Classification Rules from Database in the Context of Knowledge Acquisition and Representation," in IEEE Transactions on Knowledge & Data Engineering, vol. 3, no. , pp. 293-306, 1991.
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