System for Web Resources Content Structuring and Recognizing With the Machine Learning Elements

Abstract

Context.A large number of web resources of different organizations requires checking of relevance and correctness of the content, in particular, concerning characteristics of the organization, staff, etc. For this, it is necessary to develop a system of the automated content analysis. This task causes the need to develop a method and software for structuring and recognizing of web resources content. Existing parsing systems do not provide solving of the specified task, since they do not contain elements of machine learning. The object of the research is the process of automated analysis of the web resources content.Objective. The goal of the work is the creation of the system for web resources content structuring and recognizing. Method. The system of structuring and recognizing of text content of web resources with elements of machine learning is considered. Models of the system functioning are proposed. The architecture for realizing of software system for structuring and recognizing of text content of web resources is developed. Example of implementation of the model of developed system for structuring, recognizing and revealing of outdated and incorrect information about personnel on the web resource of educational institution is given.Results. The developed software may be used by support services in order to update and correct the information content. Conclusions. The system of structuring and recognizing of content of web resources with the machine learning elements has been considered. The proposed system compared with the known ones, ensures automated content structuring, recognizing of outdated, non-relevant or wrong information. Represented example of the structuring and recognizing of outdated and incorrect information on the website of educational institution confirms the effectiveness of the proposed system.