Hybrid categorical expert system for use in content aggregation

Abstract

The subject of this research is the development of the architecture of an expert system for distributed content aggregation system, the main purpose of which is the categorization of aggregated data. The author examines the advantages and disadvantages of expert systems, a toolset for the development of expert systems, classification of expert systems, as well as application of expert systems for categorization of data. Special attention is given to the description of the architecture of the proposed expert system, which consists of a spam filter, a component for determination of the main category for each type of the processed content, and components for the determination of subcategories, one of which is based on the domain rules, and the other uses the methods of machine learning methods and complements the first one. The conclusion is made that an expert system can be effectively applied for the solution of the problems of categorization of data in the content aggregation systems. The author establishes that hybrid solutions, which combine an approach based on the use of knowledge base and rules with the implementation of neural networks allow reducing the cost of the expert system. The novelty of this research lies in the proposed architecture of the system, which is easily extensible and adaptable to workloads by scaling existing modules or adding new ones.