Instance-driven Ontology Evolution in DL-Lite

The development and maintenance of large and complex on-tologies are often time-consuming and error-prone. Thus, au-tomated ontology learning and evolution have attracted in-tensive 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 ax-ioms from the logical consequences of a TBox, and it is w.r.t. an ABox. In this paper we introduce a model-theoretic ap-proach 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 corre-sponding complexity in propositional logic, and the problem is tractable when the size of the new data is bounded