# Ontology-Driven Semantic Analysis of Tabular Data:
## 1 Introduction  
Currently, data collection and analysis are an integral part of the vast majority of fields of activity. Information can be presented in various formats, such as text, tables, and images. At the same time, one of the critical tasks is the analysis of tables that contain large amounts of information. To extract the information needed from a table, one needs to analyze it and understand the semantics, including the content type of each cell and the relationships between them. It is important to consider that the structures of tables can differ significantly from each other. Understanding the semantics of a table makes it possible to find relationships between various data, form a general picture of the contents of the table, and extract information in a convenient form for further processing. The aim of the work is to develop and implement a technology for the semantic analysis of tables using OWL ontology [1]. OWL is a formal knowledge description language used in semantic web applications. It is used to create ontologies that describe relationships between entities and concepts in a particular field of knowledge. To further elucidate and achieve this overarching goal, several pivotal research questions have been formulated:  
1. How can the semantics of tables be effectively understood to facilitate accurate data extraction? 2. How can ontology, particularly OWL ontology, be leveraged to enhance the semantic analysis of tables?What is the efficacy of the proposed iterative algorithm in parsing table contents and determining cell types based on ontology? 4. How do advanced techniques like cosine distance search and neural network-based table subject classification augment the efficiency of our method? 5. How does our method compare in terms of versatility and adaptability to existing table extraction methods, especially in terms of language and field specificity? In the proposed research, we will try to answer these questions and provide evidence. To solve the problem of semantic analysis of tables and extract information, an iterative algorithm is proposed that performs a primary analysis of the table and determines the content type of each cell: a number or a string; for each cell containing a string, the algorithm analyzes the value of this cell and specifies its type: the cell can be an object from the ontology, a property or relationship between objects, or a third-party reference. If the cell value is an object, then the row containing that cell is marked as the row containing the found object; if the cell value is a property or relation, then the column containing that cell is marked as the column containing that property or relation; for each cell containing a number, its row is checked for an ascending sequence, which is a sequence of years. If such a sequence is found, the algorithm considers that the rows of the table should contain objects, the columns should contain years (or other consecutive time intervals), and each cell is the value of a property or object, the type of which is determined using the table subject classifier; then for each cell whose row contains an object and whose column is a property or relation, a new triplet is formed, consisting of the object, property or relation, and the value of the cell; each formed triplet is added to the ontology, which allows for a more accurate analysis of subsequent tables. The scientific novelty of this work lies in the creation of a new (automated) method for extracting data from tables in any language in an arbitrary field of knowledge--subject to the availability of an ontology that describes the data structure. Approaches are also used to classify data more accurately (such as search by cosine distance and a neural network to classify the subject of a table) in comparison with similar algorithms. While there have been various methods developed for data extraction from tables, most are either language-specific or field-specific.