# Ontology-Driven Semantic Analysis of Tabular Data:
## 6 Discussion  
The iterative algorithm developed in this study represents a significant advance in the field of table semantic analysis. Using an ontology-based approach, the algorithm can perform an initial analysis of the table, determining the content type of each cell and then refine 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 current approach illustrates a novel methodology for table semantic analysis through an iterative algorithm. Unlike conventional methods, this approach harnesses the power of ontology-based analysis, allowing for a more intricate, two-tiered procedure. In previous works, semantic analysis of tables often involved singular, straightforward analyses, which occasionally suffered from inaccuracies due to the complexities and variability of tabular data [24]. However, the newly developed algorithm initiates with an overarching examination of the table, wherein it discerns the content type of each cell. This \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Algorithms** & **Iterative Approach** & **Source Types** & **Custom Data** & **Versatility across** & **Time** & **OWL-Compatible** \\  & **Approach** & & **Format Support** & **Knowledge Domains** & **Efficiency** & **Data Exportation** \\ \hline Our solution & Yes & Excel & Yes & Yes & No & Yes \\ TableMiner+ & Yes & HTML, PDFs, XML & No & No & Yes & No \\ QTLableMiner++ & No & HTML, PDFs, XML & No & No & Yes & Yes \\ Qurna & No & HTML, PDFs & Yes & Yes & No & Yes \\ Camelot & No & PDFs & Yes & Yes & No & No \\ \hline \end{tabular}
\end{table} 
Table 3: List of properties of similar algorithms with our solution. primary step already surpasses many traditional methods by creating a preliminary yet solid framework for the next steps [25]. Furthermore, not only does this iterative algorithm ensure accurate data extraction, but it also aids in adding new data seamlessly into the ontology. This, in turn, enriches the ontology, which improves the analysis of tables in subsequent runs. It's a feedback loop that consistently improves upon itself [26]. Traditional methods, on the other hand, lacked this continuous improvement approach, which often made them stagnate in their accuracy levels over time [27]. In conclusion, the iterative algorithm is not just a minor upgrade; it's a significant advancement in the realm of table semantic analysis. By incorporating an ontology-based approach and refining data extraction through a two-tiered analysis, it addresses the pitfalls of previous works, offering a dynamic, self-improving solution for the challenges of tabular data extraction. The innovation of this research lies in its automated method of extracting data from tables in different languages and in different fields of knowledge, provided that an appropriate ontology is available. The integration of advanced techniques such as cosine distance search and neural network-based table subject classification further highlights its effectiveness. The result of this work 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 natural and economic resource tables, confirms the robustness 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.