An Alternative to Θ-Subsumption Based on Terminological Reasoning

Philipp Hanschke, Manfred Meyer

DFKI DFKI Research Reports (RR) 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.

RR-92-38.pdf (pdf, 9 MB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence