# Ontology-Driven Semantic Analysis of Tabular Data:
## 7 Conclusions  
The main scientific significance and novelty of our research lies in solving the problems of semantic analysis of tables using an innovative iterative algorithm, offering a comprehensive solution based on ontology. This is critical given the ubiquity of data in tabular format across all sectors. The practical implications of the research are enormous, as evidenced by the high accuracy of the algorithm in tests, indicating its potential usefulness in various applications. This study delved into the intricacies of table data extraction and semantic analysis, emphasizing the importance of understanding the semantics of tables in order to extract meaningful relationships and information. The main goal was to develop a technology for the semantic analysis of tables using the OWL ontology, a recognized formal knowledge description language. The proposed iterative algorithm is distinguished by its ability to perform an initial analysis of the table, determining the content type of each cell and then refining its type based on the ontology. This method ensures accurate data extraction and facilitates the addition of new data to the ontology, improving the analysis of subsequent tables. The real-world implications of our research are vast. With our method, researchers, data analysts, and industry professionals can efficiently extract and interpret data from tables, thereby streamlining data integration, analysis, and decision-making processes. For instance, our rigorous testing phase demonstrated an impressive triple extraction recall of 99.4% from tables related to water resources and socio-economic indicators of Kazakhstan. Such high accuracy indicates the algorithm's potential in diverse applications, ranging from environmental studies to socio-economic analyses. The result of this effort was a software application capable of semantically classifying tabular data. Taking a table and an ontology as input, it outputs the identified table objects and their attributes based on the knowledge structure of the ontology. This achievement paves the way for a faster transition of information from tables to ontologies, making it easier to summarize data and identify patterns. A rigorous testing phase, including 30 tables in the field of natural and economic resources, confirms the reliability and accuracy of the algorithm. Evaluation metrics, including precision, recall, and F1 score, provide a comprehensive assessment of the algorithm's performance. The results presented in Tables 1-3 demonstrate the high accuracy, completeness, and F1 measures of the algorithm. Despite typos, synonyms, or other inaccuracies that may lead to a slight decrease in completeness, including different spellings of words in the ontology can improve this indicator. In the process of testing, 8697 triplets were extracted and integrated into an ontology in which the subject, predicate and object are simultaneously defined. Given that the tables contained 8750 triplets, the triplet extraction completeness is an impressive 99.4%, well above the 95% threshold set in the testing requirements. Such noteworthy metrics have been achieved through the use of Levenshtein distance to match entities and the use of ontology as a source of information about the structure of a knowledge domain. This approach allows for high-quality object classification to be achieved without the need to make changes to the algorithm when adding new entities or changing the knowledge area. The following supporting information can be downloaded at [[https://github.com/Titrom025/PyTableMiner/blob/main/webApp/templates/index.html](https://github.com/Titrom025/PyTableMiner/blob/main/webApp/templates/index.html)]([https://github.com/Titrom025/PyTableMiner/blob/main/webApp/templates/index.html](https://github.com/Titrom025/PyTableMiner/blob/main/webApp/templates/index.html)). Conceptualization, M.M. and V.B. ; methodology, V.B. ; software, R.T.; resources and data curation, A.O. ; writing--original draft preparation, V.B. and A.O. ; writing--review and editing, M.M.