Artificial Intelligence
Knowledge base refinement and theory revision
Proceedings of the sixth international workshop on Machine learning
Explanation-Based Generalization: A Unifying View
Machine Learning
Semantic spam filtering from personalized ontologies
Journal of Web Engineering
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This paper presents an approach to retranslation, the third and final step of the theory reduction approach to solving theory revision problems [3,4]. Retranslation involves putting a modified "operationalized," or "reduced," version of the desired revised theory back into the entire language of the original theory. This step is desirable for a number of reasons, not least of which is the need to "compress" what are generally very large reduced theories into much smaller, and thus, more efficiently evaluated, unreduced theories. Empirical results for the retranslation method are presented.