Concept and Role Forgetting in ALC Ontologies

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 . 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 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 ontologies and state several important properties of forgetting in this setting. We then define the result of forgetting for concept descriptions in , 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 ontology does not exist in general, even for the special case of concept forgetting. This makes the problem of how to compute forgetting in more challenging. We address this problem by defining a series of approximations to the result of forgetting for 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.

[1]  Diego Calvanese,et al.  The Description Logic Handbook , 2007 .

[2]  Alan L. Rector,et al.  Why do it the hard way? The Case for an Expressive Description Logic for SNOMED , 2008, KR-MED.

[3]  Carsten Lutz,et al.  Conservative Extensions in Expressive Description Logics , 2007, IJCAI.

[4]  B. Parsia,et al.  Combining OWL Ontologies Using E-Connections , 2005 .

[5]  Ian Horrocks,et al.  Just the right amount: extracting modules from ontologies , 2007, WWW '07.

[6]  Frank van Harmelen,et al.  A semantic web primer , 2004 .

[7]  Willem Conradie,et al.  Definitorially Complete Description Logics , 2006, KR.

[8]  Carsten Lutz,et al.  Did I Damage My Ontology? A Case for Conservative Extensions in Description Logics , 2006, KR.

[9]  Enrico Motta,et al.  Developing Ontologies in OWL: an Observational Study , 2006, OWLED.

[10]  Boris Konev,et al.  Forgetting and Uniform Interpolation in Large-Scale Description Logic Terminologies , 2009, IJCAI.

[11]  James A. Hendler,et al.  The Semantic Web" in Scientific American , 2001 .

[12]  Frank Wolter,et al.  Modularity in DL-Lite , 2007, Description Logics.

[13]  Boris Konev,et al.  The Logical Difference Problem for Description Logic Terminologies , 2008, IJCAR.

[14]  R. Reiter,et al.  Forget It ! , 1994 .

[15]  Jeff Z. Pan,et al.  Forgetting Concepts in DL-Lite , 2008, ESWC.

[16]  Frank Wolter,et al.  Can You Tell the Difference Between DL-Lite Ontologies? , 2008, KR.

[17]  Frank van Harmelen,et al.  A Semantic Web Primer, 2nd Edition (Cooperative Information Systems) , 2008 .

[18]  Kewen Wang,et al.  Semantic forgetting in answer set programming , 2008, Artif. Intell..

[19]  Harith Alani,et al.  Winnowing Ontologies Based on Application Use , 2006, ESWC.

[20]  Ian Horrocks,et al.  A Logical Framework for Modular Integration of Ontologies , 2006 .

[21]  Enrico Motta,et al.  Identifying Key Concepts in an Ontology, through the Integration of Cognitive Principles with Statistical and Topological Measures , 2008, ASWC.

[22]  Jeff Z. Pan,et al.  Uniform Interpolation for ALC\mathcal{ALC} Revisited , 2009, Australasian Conference on Artificial Intelligence.

[23]  Jeff Z. Pan,et al.  Uniform Interpolation for ALC Revisited , 2009, Australasian Conference on Artificial Intelligence.

[24]  Jeff Z. Pan,et al.  Forgetting for knowledge bases in DL-Lite , 2010, Annals of Mathematics and Artificial Intelligence.