A Uniform Tableaux-Based Approach to Concept Abduction and Contraction in ALN

We present algorithms based on truth-prefixed tableaux to solve both Concept Abduction and Contraction in ALN DL. We also analyze the computational complexity of the problems, showing that the upper bound of our approach meets the complexity lower bound. The work is motivated by the need to offer a uniform approach to reasoning services useful in semantic-based matchmaking scenarios.

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