Instance-Driven Ontology Evolution in DL-Lite

The development and maintenance of large and complex ontologies are often time-consuming and error-prone. Thus, automated ontology learning and evolution have attracted intensive research interest. In data-centric applications where ontologies are designed from the data or automatically learnt from it, when new data instances are added that contradict the ontology, it is often desirable to incrementally revise the ontology according to the added data. In description logics, this problem can be intuitively formulated as the operation of TBox contraction, i.e., rational elimination of certain axioms from the logical consequences of a TBox, and it is w.r.t. an ABox. In this paper we introduce a model-theoretic approach to such a contraction problem by using an alternative semantic characterisation of DL-Lite TBoxes. We show that entailment checking (without necessarily first computing the contraction result) is in coNP, which does not shift the corresponding complexity in propositional logic, and the problem is tractable when the size of the new data is bounded.

[1]  Ljiljana Stojanovic,et al.  Consistent Evolution of OWL Ontologies , 2005, ESWC.

[2]  Maurice Pagnucco,et al.  Model Based Horn Contraction , 2012, KR.

[3]  Johanna Völker,et al.  A Kernel Revision Operator for Terminologies - Algorithms and Evaluation , 2008, International Semantic Web Conference.

[4]  Diego Calvanese,et al.  Updating TBoxes in DL-Lite , 2010, Description Logics.

[5]  Boris Konev,et al.  Exact Learning of TBoxes in EL and DL-Lite , 2013, Description Logics.

[6]  Guilin Qi,et al.  A Survey of Revision Approaches in Description Logics , 2008, Description Logics.

[7]  André Fuhrmann,et al.  A survey of multiple contractions , 1994, J. Log. Lang. Inf..

[8]  Yue Ma,et al.  Concept adjustment for description logics , 2013, K-CAP.

[9]  Diego Calvanese,et al.  Evolution of DL-Lite Knowledge Bases , 2010, SEMWEB.

[10]  Guilin Qi,et al.  Approximating Model-Based ABox Revision in DL-Lite: Theory and Practice , 2015, AAAI.

[11]  H. Kyburg.  The Fixation of Belief and Its Undoing , 1994 .

[12]  Renata Wassermann,et al.  Base Revision for Ontology Debugging , 2009, J. Log. Comput..

[13]  Kewen Wang,et al.  A New Approach to Knowledge Base Revision in DL-Lite , 2010, AAAI.

[14]  Ken Satoh.  Nonmonotonic Reasoning by Minimal Belief Revision , 1988, FGCS.

[15]  Georg Gottlob,et al.  On the complexity of propositional knowledge base revision, updates, and counterfactuals , 1992, Artif. Intell..

[16]  Bijan Parsia,et al.  Repairing Unsatisfiable Concepts in OWL Ontologies , 2006, ESWC.

[17]  Catia Pesquita,et al.  Predicting the Extension of Biomedical Ontologies , 2012, PLoS Comput. Biol..

[18]  James P. Delgrande,et al.  Belief revision in Horn theories , 2015, Artif. Intell..

[19]  Guilin Qi,et al.  Contraction and Revision over DL-Lite TBoxes , 2014, AAAI.

[20]  Frank Wolter,et al.  Logic-based ontology comparison and module extraction, with an application to DL-Lite , 2010, Artif. Intell..

[21]  Jennifer Golbeck,et al.  Modeling a description logic vocabulary for cancer research , 2005, J. Biomed. Informatics.

[22]  Bernhard Ganter,et al.  Completing Description Logic Knowledge Bases Using Formal Concept Analysis , 2007, IJCAI.

[23]  Evgeny Kharlamov,et al.  Ontology Evolution Under Semantic Constraints , 2012, KR.

[24]  Jeff Z. Pan,et al.  Inconsistencies, Negations and Changes in Ontologies , 2006, AAAI.

[25]  Bijan Parsia,et al.  Laconic and Precise Justifications in OWL , 2008, SEMWEB.

[26]  Jens Lehmann,et al.  Concept learning in description logics using refinement operators , 2009, Machine Learning.

[27]  Sebastian Rudolph,et al.  Interactive ontology revision , 2012, J. Web Semant..

[28]  Frank van Harmelen,et al.  Debugging Incoherent Terminologies , 2007, Journal of Automated Reasoning.

[29]  Hirofumi Katsuno,et al.  Propositional Knowledge Base Revision and Minimal Change , 1991, Artif. Intell..

[30]  Diego Calvanese,et al.  The DL-Lite Family and Relations , 2009, J. Artif. Intell. Res..

[31]  Diego Calvanese,et al.  Tractable Reasoning and Efficient Query Answering in Description Logics: The DL-Lite Family , 2007, Journal of Automated Reasoning.