An alternative to theta-subsumption based on terminological reasoning

Abstract

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 Theta-subsumption.