Decomposition of Fuzzy Homogeneous Classes of Objects - Terletskyi, Dmytro O.; Yershov, Sergey V. (Information and Software Technologies, 2022)
Extraction of new knowledge from earlier obtained and integrated knowledge is one of the main stages of intelligent knowledge analysis. To handle such a task, a knowledge-based system should be able to decompose complex or composite knowledge structures and extract new knowledge items, which were hidden or non-obvious before. Existed approaches to decomposition within object-oriented paradigm provide different variants of partitioning or fragmentation of main knowledge structures, such as objects, classes, and relations among them, however, most of them do not consider semantic structural and functional dependencies among properties and methods of classes that affect on the decomposition process. In this paper, we introduced concepts of fuzzy structural and functional atoms, as well as molecules of fuzzy homogeneous classes of objects, within such a knowledge representation model as fuzzy object-oriented dynamic networks. In addition, we proposed the algorithm for the decomposition of fuzzy homogeneous classes of objects, which implements the idea of universal decomposition exploiter of fuzzy classes of objects, and constructs semantically correct subclasses of a fuzzy homogeneous class of objects by solving appropriate constraint satisfaction problem that defines decomposition conditions. To demonstrate some possible application scenarios, we provided an appropriate example of the decomposition of a fuzzy homogeneous class of objects.