Semantic models and tools for the development of artificial neural networks and their integration into knowledge bases

Abstract

Objectives. Specifications of models and tools for the development of artificial neural networks (ANNs) and their integration into knowledge bases (KBs) of intelligent systems are being developed. The relevance is determined by the necessity of implementing the possibility to solve complex problems by intelligent systems, which algorithms and methods of solving are not available in the knowledge base of the intelligent system.

Methods. Four levels of integration of artificial neural networks into knowledge bases are formulated and analyzed. During the analysis the requirements and specifications for required models and tools for the development and integration are formulated. Specified at each level the models and tools include the models and tools of previous level. The application of the tools is considered by the example of solving the problem of classifying the knowledge base entities using a graph neural network.

Results. The specifications of the ANN representation model in the knowledge base, the agent-based model for the development and interpretation of the ANN, which ensures the integration of the ANN into knowledge bases at all selected levels, as well as the method for classifying knowledge base entities using a graph neural network, have been developed.

Conclusion. The developed models and tools allow integrating any trained ANNs into the knowledge base of the intelligent system and using them to solve complex problems within the framework of OSTIS technology. It also becomes possible to design and train ANNs both on the basis of external data and on the basis of fragments of the knowledge base. Automation of ANNs development process in the knowledge base becomes available.