Intelligent Design of Class Structure Model based on Ontological Data Analysis
This paper investigates a formal approach which supports a critically significant step in object oriented analysis and software engineering. It is proposed to create an object class structure model based on an Ontological Data Analysis of a targeted domain empirical data. This technology is a development of the well-known method of Formal Concept Analysis and is able to work with incomplete (contradictory, inaccurate, vague, etc.) empirical information on domain, naturally supports the construction of arbitrary binary relationships between classes of objects and takes into account available to researcher information about the interconnection between actual for the designer domain objects properties. Multi-valued vector logic models and means are usedin order to factor in the realities of the empirical data accumulation.In concurrence with this a nonstrict formal context is being formed to display the conceptual domain structure. In this context truth values of basic semantic proposition of the form “x object has y property” are presented in a vector form. Its transformation into a binary formal context, for which formal concepts output effective algorithms are known, is done using intellectual alpha approximation algorithm which takes into account typical relationships between the objects properties and, above all, a conceptual conjugation of object properties arising from the fundamental cognitive designer’s procedures – conceptual scaling of the objects properties detected. A properties inclusion partial order between derived from the context formal concepts appears which is known as inheritance of properties in object-oriented analysis. Determined by this ratio a formal conceptclosed lattice is transformed into a model that describes an objects class structure, according to a number of pragmatic design principles of this key software component.
Proceedings of the Institute for System Programming, vol. 27, issue 3, 2015, pp. 73-86
ISSN 2220-6426 (Online), ISSN 2079-8156 (Print).
DOI: 10.15514/ISPRAS-2015-27(3)-5Full text of the paper in pdf Back to the contents of the volume