Vladislavs Nazaruks

Retrieving the Topology from the Knowledge Frame System for Composition of the Topological Functioning Model.

Model-driven software development considers models as a core artefact for generation of software source code. This requires models to be formal and complete enough for further transformations and code generation. It requires clear understanding of such knowledge as functionality, objects and dependencies in the problem domain. In our approach, this knowledge is kept in the frame-based system. The completeness and consistency of the knowledge can be verified by generating and validating the topological functioning model (TFM). The TFM is a model, which elements are linked by the topology, i.e. by cause and effect relations among the functional characteristics of the domain. Automated composition of the TFM requires retrieving appropriate conditions on cause and effect functional characteristics of the system from the knowledge base. The proposed algorithm reads data of functional characteristics kept in the knowledge base, relates those of them, where a cause condition corresponds to an effect condition, and generates data for the corresponding cause-and-effect relation. The difficulty is that conditions can be combined using logical operators AND, OR, XOR, as well as can use negation NOT. The benefit is that any inconsistency in the retrieved topology could be discovered and marked for further analysis. This should force careful analysis of the problem domain before generation of the design model. That could lead to decreasing a number of errors made due to uncertainty in the analysis.

Verification of Causality in the Frame System based on the Topological Functioning Modelling

Causality is universal relations among phenomena (states, facts, elements, functions) in the system. Verification of causality in the knowledge frame system based on principles of the topological functioning modelling can help in discovering inconsistencies such as incompleteness, ambiguity or contradictions in knowledge on system’s functioning. The method for such verification is presented in this paper. It is based on topological and functioning properties of the topological functioning model including the definition of continuous mapping between topological spaces. The method helps in discovering inconsistent combinations of cause-and-effect relations or a lack of them. Functional characteristics of the system involved in these relations are marked as doubtful. The results of verification require additional investigation by a software developer. A use of the proposed method can lead to more thorough system analysis before development of the solution.

Joint Usage of Frames and the Topological Functioning Model for Domain Knowledge Presentation and Analysis

Joint usage of frames and Topological Functioning Model (TFM) provides proper analysis of knowledge in the domain under study. The main issue in domain knowledge analysis is completeness of discovered knowledge. Formal representation of the knowledge in frames allows automated construction and validation of the TFM, thus allowing to discover white places in knowledge. Analysing TFM metamodels, the structure of the frame system for generation of the TFM is proposed. The frame system leads to highlighting structural knowledge, while validation of the generated TFM shows white places in behavioural knowledge. Validation of the TFM does not guarantee the complete identity of obtained knowledge to the domain, since the knowledge is based on expert opinions. Thus, analysis of the problem domain is shifted from the separate investigation of dynamic and structural aspects of the system to holistic understanding of domain phenomena. The presented results should be refined if other derived models are added.

A Survey on Domain Knowledge Representation with Frames

Domain knowledge acquisition, presentation and maintenance play an important role in software development. Frame-based knowledge bases are used to support the decision-making process. We believe that a use of a knowledge base that supports model transformations is not less important. To clarify the current state of a use of frame systems we have investigated recent research in the field to find out about techniques used for knowledge acquisition, frame elements, implementation technologies, existing limitations in implementation and integration with other knowledge representation formats. The overview showed that knowledge acquisition often is manual, procedural knowledge in frames can be separated, web-enabled knowledge bases are the trend, and the frame systems can be used in hybrid knowledge bases. However, some limitations in performance and integration with other knowledge representation systems exist due to support of different world paradigms. The obtained results show that despite existing limitations, frame-based knowledge systems still are in use and researchers found ways how to adapt them to the modern requirements.