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An Alternative to Θ-Subsumption Based on Terminological Reasoning

Philipp Hanschke; Manfred Meyer
DFKI, DFKI Research Reports (RR), Vol. 92-38, 1992.


Clause subsumption and rule ordering are long-standing research topics in machine learning (ML). Since logical implication can be reduced to rule-subsumption, the general subsumption problem for Horn clauses is undecidable [Plotkin, 1971b]. In this paper we suggest an alternative knowledge-representation formalism for ML that is based on a terminological logic. It provides a decidable rule-ordering which is at least as powerful as Θ-subsumption.