# Ontology-Driven Semantic Analysis of Tabular Data:
## 2 Related Works
#### System Analysis of Presented Solutions  
Thus, the developed algorithm should combine the advantages of existing solutions but not have their disadvantages. The main feature of the solution should be the versatility of its application. To do this, a modular approach is proposed--for each file type (XLSX, CSV, PDF), an appropriate processing module is created that will extract the table from the file and pass it on for analysis. This approach makes it easy to add support for new file types. An important requirement resulting from universality is the ability to process tables in an arbitrary area of knowledge. To this end, the module must use an OWL ontology that describes the structure of the knowledge area. It lists the main types of objects, their properties and relationships between them. This approach will allow processing tables in any area of knowledge, provided that the created OWL ontology is available. When using ontologies in table analysis, tables can be mapped to specific classes and properties described in the ontology. For example, if a table contains information about different types of animals, then this table can be associated with the Animals' ontology class, and the columns of the table can be associated with the corresponding properties, such as \"species\" or \"habitat\". Approaches using ontologies are an effective solution for data classification since these approaches provide additional information about the structure of a knowledge area. One example of the use of ontologies is the algorithm described in [19], which uses an ontology to train a neural network in order to perform a semantic analysis of tables and create knowledge graphs based on data from a table. This solution has a high classification accuracy but requires retraining of the algorithm when working in a new area of knowledge. Ontological approaches to the semantic analysis of tables have several advantages. They provide the opportunity to obtain a higher accuracy of analysis since ontologies provide a formal way to define the conceptual relationships between objects and concepts. In addition, ontologies can be used to automatically create rules for parsing tables, which reduces the need for manual rule creation. However, ontology-based approaches also have disadvantages. For example, ontologies can be complex and require significant time and effort to create and maintain. In addition, the use of ontologies can be limited to the subject area for which the ontology was created, which makes the approach based on this ontology less universal. In general, ontology-based approaches can be useful in cases where high accuracy of analysis is required and when an ontology already exists or can be created for the subject area in which the program for semantic analysis of tables works. For example, there is a solution described in the article [20] which allows the creation of an ontology based on tabular data. After running the algorithm, this ontology will be automatically expanded, then properties, relations between objects, and additional classes of objects will be added. Very often, even when using ontologies, one cannot avoid heuristic ideas about the structure and semantics of tables. If the solution uses such heuristic representations, then it relies on predefined rules to extract information from the tables. These rules can be manually created by subject matter experts or can be automatically extracted from large datasets using machine learning techniques. Rules can describe various aspects of tables, such as structure, formatting, and cell content. For example, a rule might say, \"if the first column of a table contains dates, then the second column must contain the corresponding values.\" non-standard tables or changes in the structure and formatting of tables. Hybrid approaches to the semantic analysis of tables combine several methods and technologies to achieve more accurate and complete data extraction from tables. Hybrid methods inherently exhibit superior adaptability, accommodating varied document types and formats.