# A Novel Approach to Data Extraction on Hyperlinked Webpages
## 5 Conclusions and Future Work  
Much of the information on the WWW is presented in unstructured documents [36]. A large amount of relational data exist as tables which are often hyperlinked, providing additional meaning to the data. Lots of work has been done to improve structured extraction [25; 37; 38] and data cleaning [27]; however, there is still room for improvement in extraction tools. The biggest and most fundamental challenge which has not been fully achieved yet is maintaining the coverage and accuracy of the extraction of a schema for hyperlinked tables. Hence, software architectures need to be enhanced so that they can provide a deeper connection between the users and web tables. Our schema extraction for data that is hyper-linked on various webpages is an attempt to improve the querying results through search engines. We developed an automatic algorithm that extracts data in web tables, and classifies tables as relational or non-relational, and simple or complex. For relational \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Actual Class\(\backslash\)Predicted Class** & **Links Having Real Tables** & **Links Having Simple Text** & **Total** \\ \hline Links having Real Tables & 2235 & 1365 & 3600 \\ \hline Links having Simple Text & 90 & 2310 & 2400 \\ \hline Total & 2325 & 3675 & 6000 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Confusion matrix of linked tables. Figure 5: A comparison of five different algorithms. (**A**) Accuracy in the identification of header and data rows. (**B**) Accuracy in the identification of real tables. tables, it identifies the primary and secondary tables by checking the hyperlink direction. Primary tables are given an identity which is used as a foreign key in the secondary tables. We believe this work will help to improve the effectiveness of queries using SQL and future benchmarking efforts [39]. Relational schema generated for such linked tables should be beneficial for replying to better and more complex queries. A major problem was faced in extracting the schema of linked tables when links appeared at the beginning or end of the table, where no data row was identified. Though we have achieved very high precision, we only achieved a modest recall which needs further research and work. Application of our research is not limited to one domain; its application span is wider. If a researcher needs to build any domain specific corpus, then our proposed method can be helpful. Further, it will also enhance the search optimization on search engines such as Google. For example, if we query 'Postal Codes of US States' on Google, the query will return us the information in a tabular form. However, if we query 'Professor with machine learning interest,' instead of returning us the result in a tabular form, the results return us the URLs of profiles of different university faculty members having machine learning as their interest. Professors' information and interests are available on faculty pages that may look like tables but are not in a relational structure. Thus, such information is isolated from further interconnection. [PERSON] et al. attempted to handle this but they have only handled simple table structure. We have handled both complex and simple tables. Therefore, we made our dataset available at [[https://sourceforge.net/projects/linked-web-table/for](https://sourceforge.net/projects/linked-web-table/for)]([https://sourceforge.net/projects/linked-web-table/for](https://sourceforge.net/projects/linked-web-table/for)) other researchers. In the future, we also aim to identify different linking possibilities of tables formed by CSS or dynamic JavaScript and to build a database system by creating the linked relational tables accordingly. This will grant more accessibility to the huge amounts of content on the World Wide Web that are available in the web tables. Data analysis and initial draft: K.S. ; Methodology: K.S., N.M., and M.K. ; First draft preparation, K.S. and M.K. ; Writing--Reviewing and editing, K.S., N.M., and M.K. ; Supervision: N.M. and M.K.