Concept and Role Forgetting in ${\mathcal {ALC}}$ Ontologies

• Authors: Kewen Wang;Zhe Wang;Rodney Topor;Jeff Z. Pan;Grigoris Antoniou
• Venue: ISWC '09 Proceedings of the 8th International Semantic Web Conference
• Year: 2009

Forgetting is an important tool for reducing ontologies by eliminating some concepts and roles while preserving sound and complete reasoning. Attempts have previously been made to address the problem of forgetting in relatively simple description logics (DLs) such as DL-Lite and extended ${\mathcal {EL}}$. The ontologies used in these attempts were mostly restricted to TBoxes rather than general knowledge bases (KBs). However, the issue of forgetting for general KBs in more expressive description logics, such as ${\mathcal {ALC}}$ and OWL DL, is largely unexplored. In particular, the problem of characterizing and computing forgetting for such logics is still open. In this paper, we first define semantic forgetting about concepts and roles in ${\mathcal {ALC}}$ ontologies and state several important properties of forgetting in this setting. We then define the result of forgetting for concept descriptions in ${\mathcal {ALC}}$, state the properties of forgetting for concept descriptions, and present algorithms for computing the result of forgetting for concept descriptions. Unlike the case of DL-Lite, the result of forgetting for an ${\mathcal {ALC}}$ ontology does not exist in general, even for the special case of concept forgetting. This makes the problem of how to compute forgetting in ${\mathcal {ALC}}$ more challenging. We address this problem by defining a series of approximations to the result of forgetting for ${\mathcal {ALC}}$ ontologies and studying their properties and their application to reasoning tasks. We use the algorithms for computing forgetting for concept descriptions to compute these approximations. Our algorithms for computing approximations can be embedded into an ontology editor to enhance its ability to manage and reason in (large) ontologies.