# Ontology-Driven Semantic Analysis of Tabular Data:
## 6 Discussion
During testing, 8697 triples were extracted and integrated into an ontology in which the subject, predicate and object are simultaneously defined. Considering that the tables contained 8750 triples, the completeness of triple extraction is an impressive 99.4%, well above the 95% threshold set in the testing requirements. Such notable metrics have been achieved through the use of Levenshtein distance for matching entities and the use of ontology as a source of information about the structure of the knowledge area. This approach allows for achieving a high-quality object classification without the need to make changes to the algorithm when adding new entities or changing the knowledge area. However, like any innovative solution, continuous improvement and testing in various scenarios will only improve its capabilities. It would be interesting to know how the algorithm scales with large datasets and how it performs across different domains. The potential for integrating this algorithm with other systems, such as databases, content management systems, or even web crawlers, can be explored. Implementing a feedback mechanism for users can help further improve the algorithm. While our results have been promising, especially in the field of water resources and socio-economic indicators of Kazakhstan, we do recognize that every algorithm has its boundaries. The following are the limitations:  
1. Data Dependency on Ontologies: Our method's reliance on ontologies is both its strength and limitation. The quality and comprehensiveness of the ontology directly affect the performance of the algorithm. The absence of a robust ontology for a particular field might limit the adaptability of our approach in such contexts. 2. Complexity of Table Structures: Though our algorithm successfully parsed varied table structures during our tests, there may be instances of highly irregular or complex tables that could challenge the algorithm's performance. Scalability: While our tests included 30 tables, larger-scale extraction tasks, especially those involving thousands of tables, have not been rigorously tested. * Language Generalization: Even though our method can handle tables in any language, certain linguistic nuances or rare terminologies might affect the precision of data extraction in lesser-known languages. The future perspectives and research lines arising from this work are manifold: 1. Algorithm Enhancement for Complex Tables: One potential research avenue is refining our iterative algorithm to handle even more complex and irregular table structures. 2. Integration with Machine Learning Models: There's a promising direction in integrating state-of-the-art machine learning models to improve data classification further, especially in recognizing linguistic nuances across various languages. 3. Scalability Tests: We envision extensive scalability tests to ascertain the algorithm's performance on larger datasets. 4. Expanding Ontology Repositories: Given our method's reliance on ontologies, there's a valuable line of research in developing comprehensive ontologies across various fields. This will benefit not only our method but also other ontology-reliant technologies. 5. Feedback Mechanisms: Implementing a feedback mechanism where the software application learns from any inaccuracies and refines its data extraction techniques in real time could be another exciting avenue to explore. 6. Collaborative Features: Considering the practical application of our software, introducing collaborative features, allowing teams to work simultaneously and make annotations, would make it even more useful for larger projects. In conclusion, the iterative algorithm developed in this study has significant potential in the field of semantic analysis of tables. Its modular approach, combined with cutting-edge technology, makes it stand out from the crowd. The high accuracy and completeness achieved during the test phase highlight its potential as a robust and versatile tool for semantic analysis of tables in various domains.