Abstract: The semantic markups of the World Wide Web have accumulated a large amount of data and their number continues to grow. However, the potential of these data is, in our opinion, not fully utilized. The semantic markups contents are widely used by search systems, partly by social networks, but the usual approach to using that data by application developers is based on converting data to RDF standard and executing SPARQL queries, which requires good knowledge of this language and programming skills. In this paper, we propose to leverage the semantic markups available on the Web to automatically incorporate their contents to the content of other web pages. We also present a software tool for implementing such incorporation that does not require a web page developer to have knowledge of any programming languages ​​other than HTML and CSS. The developed tool does not require installation, the work is performed by JavaScript plugins. Currently, the tool supports semantic data contained in the popular types of semantic markups “microdata” and JSON-LD, in the tags of HTML documents and the properties of Word and PDF documents.

Abstract:

The development of an application based on machine learning methods for automatic annotation of web pages according to the Microdata standard is described, with the possibility of extension to other standards and injecting data to JSX files. Datasets were collected and prepared for training Machine Learning (ML) models. The ML model metrics were collected and analyzed.