Semantically Consistent Intersection of Fuzzy Homogeneous Classes of Objects

The intersection of fuzzy classes makes it possible to create new classes of objects by discovering common subclasses. Such an approach allows computing of concept similarity for extracted fuzzy knowledge pieces with knowledge already integrated into the knowledge base, ensuring effective knowledge integration. However, the intersection of fuzzy classes can produce semantically inconsistent fuzzy classes, which contradict internal dependencies between their attributes. Therefore, we have improved the algorithm for the intersection of fuzzy classes using their internal dependencies and semantically consistent decomposition. The algorithm computes the intersection of fuzzy classes as the consistent largest common subclass of classes and avoids creating semantically inconsistent fuzzy classes. We presented a comprehensive example of a semantically consistent intersection of fuzzy classes to show the application of the algorithm. The proposed approach introduces a distinction between the direct intersection of sets of class attributes and the intersection based on internal semantic dependencies between class attributes.