## 6 Database Creation and Structure
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **Comments** & **Entity** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{4}{*}{Model \#1} & \multirow{4}{*}{Tok2 vec model} & Ents & 0.6202 & 0.6466 & 0.6331 \\  & & PER & 0.7804 & 0.8276 & 0.8033 \\  & & before adjusting & LOC & 0.5337 & 0.5471 & 0.5403 \\  & & the work-of-art & WOA & 0.1103 & 0.0947 & 0.1019 \\  & & DATE & 0.7280 & 0.9032 & 0.8062 \\  & & ORG & 0.3272 & 0.3333 & 0.3302 \\ \hline \multirow{4}{*}{Model \#2} & \multirow{4}{*}{Transformer-based} & Ents & 0.7718 & 0.7806 & 0.7761 \\  & & PER & 0.7994 & 0.8214 & 0.8102 \\ \cline{1-1}  & & model after & LOC & 0.5361 & 0.5597 & 0.5476 \\ \cline{1-1}  & & adjusting the & WOA & 0.8637 & 0.6987 & 0.7725 \\ \cline{1-1}  & & work-of-art entities & DATE & 0.7306 & 0.8930 & 0.8037 \\ \cline{1-1}  & & ORG & 0.3142 & 0.2868 & 0.2962 \\ \hline \multirow{4}{*}{Model \#3} & \multirow{4}{*}{Tok2 vec model} & Ents & 0.8742 & 0.8367 & 0.8550 \\  & & PER & 0.8850 & 0.8674 & 0.8761 \\ \cline{1-1}  & & LOC & 0.6412 & 0.5283 & 0.5793 \\ \cline{1-1}  & & treated on the & WOA & 0.9030 & 0.9114 & 0.9114 \\ \cline{1-1}  & & DATE & 0.8764 & 0.7880 & 0.8299 \\ \cline{1-1}  & & ORG & 0.7352 & 0.4629 & 0.5681 \\ \hline \multirow{4}{*}{Model \#4} & \multirow{4}{*}{Transformer-based} & Ents & 0.9020 & 0.8885 & 0.8952 \\ \cline{1-1}  & & PER & 0.9357 & 0.9004 & 0.9177 \\ \cline{1-1}  & & model trained on & LOC & 0.8478 & 0.7358 & 0.7878 \\ \cline{1-1}  & & the checked & WOA & 0.8577 & 0.9083 & 0.8823 \\ \cline{1-1}  & & dataset & DATE & 0.9077 & 0.8909 & 0.8992 \\ \cline{1-1}  & & ORG & 0.6949 & 0.7592 & 0.7256 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of training multiple NER models with scores corresponding to each entity type. (LOC), and Works of Art (WOA). These separate tables ensure an organized and efficient storage solution. Inside each entity type table, we store the unique entities we found in the text under this entity type. * **Link Tables**: To achieve connections between the main tables that contain all the extracted entities and the separate tables for each entity type, we built these link tables. These tables act as connectors between entities and their corresponding entity type tables. These link tables help to create a complete and integrated dataset. The importance of building this database can be summarized as follows:  
* **Efficient Storage and Retrieval**: This database will allow the user to easily and effectively store the extracted entities while maintaining a well-organized form. The structured database will allow for rapid access for entities and the rapid extraction of information. * **Facilitating Future Enhancements**: The database will act as a fundamental structure when we add more data to the dataset--like external URLs--over time. This gives users the freedom to easily add more data and expand the database. It will make the expansion of the stored information easier for future adjustments. * **Enhancing User Experience**: Users can do thorough searches in this database to find relevant data linked to a certain entity. Users may quickly access an enormous amount of knowledge on people, places, businesses, and artistic creations listed in the Encyclopedia of A.S. Pushkin by doing database queries. The database facilitates in-depth investigation and study while greatly enhancing the user experience. In a nutshell, the well-organized database we built plays a crucial part in the approach to creating a linked corpus, facilitating and enhancing the process of storing, retrieving, and searching for entities. Also, this database is considered to be a base for building more advanced applications, such as a web service that utilizes this database to enhance the user experience.