General Concept Inclusion Absorptions for Fuzzy Description logics: A First Step

In spite of this ambiguity, it should be expected that a change log contains concept names that were directly changed, and this is what we aim to find out in our next experiment;

In this paper, we have provided a formal characterization of Concept Learning in DLs according to a declarative modeling language which abstracts from the specific algorithms used

The method follows the machine learning approach known as Inductive Logic Programming (ILP) by adapting known results in ILP concerning crisp rules to the novel case of fuzzy

As future work we will study more expressive logics than ALCH, applying the ideas in the previous work DLs [3,5,6], with the aim of providing the theoretical basis of a fuzzy

We present in detail the proposed optimization techniques, we discuss their applicability to fuzzy DLs using other fuzzy operators than those used in f KD -SHIN , and we optimize

The contribution of the present paper consists of (i) proving that SHIO + is decidable and it has the finite model property by providing a upper bound on the size of models

Finally in this section, other characters of X ALC and BALC l are summarized as follows: (1) the temporal operators in X ALC and BALC l are only applied to concepts and ABox

classical negation can be satisfied, which is not the case for QC ALC; (2) we properly introduce the weak and strong satisfactions in QC SHIQ, which avoids redundant definition of