Consequence Finding in ALC.
Meghyn Bienvenu · 2007
Research on reasoning in description logics has traditionally focused on the standard inference tasks of (un)satisfiability, subsumption, and instance checking. In recent years, however, there has been a growing interest in so-called non-standard inference services (cf. [1]) which prove useful in the creation, evolution, and utilisation of description logic knowledge bases (KBs). In this paper, we propose consequence finding as a new non-standard reasoning task for description logics. As its name suggests, consequence finding is concerned with the generation of a subset of the implicit consequences of a KB. This topic has been extensively studied in propositional logic (cf. [2]) where it has been shown to be relevant to a number of areas of AI, among them knowledge compilation, abduction, and non-monotonic reasoning. In the context of description logics, we view consequence finding as a tool to enable knowledge engineers and end users alike to better understand and access the contents of a description logic KB. We consider two possible applications: