## 1 Introduction  
A large portion of the information accumulated and utilized by humanity is not machine-readable, meaning that it cannot be directly interpreted (understood) by a computer program. This includes textual, graphical, audiovisual, and other types of information. Undoubtedly, there are tools that facilitate human interaction with such information. These include various means of indexing, searching, transforming, and even recognizing patterns. However, all of these tools do not allow for the interpretation of information--only a human being is capable of doing so. Machine-readable information includes all structured formats. This primarily includes information stored in relational and non-relational databases, as well as documents represented in XML format with semantic markup. A special place among the ways of representing machine-readable information is occupied by semantic technologies or the Semantic Web--a set of technologies for representing and using conceptualized information in electronic form. The foundation of the Semantic Web is the so-called \"ontological\" approach, which is based on representing any information in the form of a semantic graph of arbitrary structure, with concepts as nodes and relationships as edges. Ontological modeling and the Semantic Web represent the next step in the evolution of machine-readable information representation methods. They provide the capability to implement conceptual models in electronic form, the logic of which is akin to human reasoning. In addition to storing information, semantic models enable logical inference based on rules. The ontological approach, based on Semantic Web technology, offers well-known advantages in designing data models for information systems:  
1. An ontological model can be easily expanded and supplemented, including integration with models from other domains. This allows it to evolve as necessary. 2. An ontological model can be developed and used collaboratively by different organizations and expert groups. The use of namespaces allows for the division of development into parts corresponding to different knowledge domains. The primary global idea of the Semantic Web is to transform the web into a global database with a semantic structure, making the data understandable for humans, as well as machine-readable. However, the ontological approach is also applied to solve specific problems in various domains:  
1. The construction of knowledge bases with the capability for logical inference. 2. Semantic search. 3. The publication of linked open datasets. Given that a large portion of the global information corpus is stored in an unstructured format, a critical task is the creation of effective methods for converting this information into a structured, machine-readable form. Assuming that the Semantic Web represents the most promising model for representing machine-readable information, it is necessary to develop methods for populating ontologies with information extracted from natural language text. To extract data from text, various techniques and methods can be employed. Information extraction (IE) systems play a crucial role in identifying and extracting meaningful data from unstructured and semi-structured data sources [1]. These systems aim to identify a predefined set of concepts within a specific domain, while filtering out irrelevant information [1]. Web data extraction systems are particularly valuable in this context, as they enable the extraction of data from web sources, including HTML web pages, and can encompass elements within the page, as well as the full text of the page itself [2]. Furthermore, the process of data extraction from web sources has led to the development of various techniques, such as automatic web news extraction using tree edit distance [3], web data extraction based on partial tree alignment [4], and intelligent self-repairable web wrappers [5].