An ExpTime Upper Bound for ALC with Integers

Author(s):  
Nadia Labai ◽  
Magdalena Ortiz ◽  
Mantas Šimkus

Concrete domains, especially those that allow to compare features with numeric values, have long been recognized as a very desirable extension of description logics (DLs), and significant efforts have been invested into adding them to usual DLs while keeping the complexity of reasoning in check. For expressive DLs and in the presence of general TBoxes, for standard reasoning tasks like consistency, the most general decidability results are for the so-called ω-admissible domains, which are required to be dense. Supporting non-dense domains for features that range over integers or natural numbers remained largely open, despite often being singled out as a highly desirable extension. The decidability of some extensions of ALC with non-dense domains has been shown, but existing results rely on powerful machinery that does not allow to infer any elementary bounds on the complexity of the problem. In this paper, we study an extension of ALC with a rich integer domain that allows for comparisons (between features, and between features and constants coded in unary), and prove that consistency can be solved using automata-theoretic techniques in single exponential time, and thus has no higher worst-case complexity than standard ALC. Our upper bounds apply to some extensions of DLs with concrete domains known from the literature, support general TBoxes, and allow for comparing values along paths of ordinary (not necessarily functional) roles.

2015 ◽  
Vol 10 (4) ◽  
pp. 699-708 ◽  
Author(s):  
M. Dodangeh ◽  
L. N. Vicente ◽  
Z. Zhang

Author(s):  
Federico Della Croce ◽  
Bruno Escoffier ◽  
Marcin Kamiski ◽  
Vangelis Th. Paschos

10.29007/jhtz ◽  
2019 ◽  
Author(s):  
Magdalena Ortiz

Reverse engineering queries from given data, as in the case of query-by-example and query definability, is an important problem with many applications that has recently gained attention in the areas where symbolic artificial intelligence meets learning. In the presence of ontologies this problem was recently studied for Horn-ALC and Horn-ALCI. The main contribution of this paper is to take a first look at the case of DL-Lite, to identify cases where the addition of the ontology does not increase the worst-case complexity of the problem. Unfortunately, reverse engineering conjunctive queries is known to be very hard, even for plain databases, since the smallest witness query is known to be exponential in general. In the light of this, we outline some possible research directions for exploiting the ontology in order to obtain smaller witness queries.


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