## 5 Conclusions and Future Work  
This work presented a novel semantic focused web crawler (SFWC) based on a knowledge representation schema (KRS), as an alternative to traditional SFWCs that use domain ontologies designed by human experts. The KRS has the feature to model any domain, is less complex, less formal, and easier to build than an ontology. The KRS describes the most relevant elements in the domain and can be automatically constructed through a semantic analysis of an input corpus. Even with a relatively low number of input web pages used to construct the corpus, as it was the case with the Wikipedia pages in this work, the average results are promising as the SFWC was able to filter relevant web pages with a score above 70%, endorsed by human raters.  
As part of the mechanisms for the SFWC to filter web pages, a new metric was used, by combining the IDF and statistical measures. The achieved results demonstrated the high capacity (above 69%) of the proposed SFWC to filter relevant web page content based on a quantitative and qualitative evaluation, being more effective with specialized topics such as the diabetes topic whose vocabulary terms have a close relation among them and thus the content of web pages associated with that domain.  
The quantitative and qualitative results demonstrate that the proposed SFWC reaches a substantial agreement between the human raters, obtaining better results (with a score above 80%) with the diabetes topic, which is more specific than the politics and computer science topics (score above 70%).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Corpus**} & \multicolumn{2}{c}{**Wikipedia**} & **Google** \\ \cline{2-3}  & \multicolumn{2}{c}{**(\(\kappa\) value)**} \\ \hline Computer science & 0.68 & 0.53 \\ Diabetes & 0.65 & 0.62 \\ Politics & 0.63 & 0.57 \\ \hline \hline \end{tabular}
\end{table}
Table 13: \(\kappa\) value for seed URLs from Wikipedia and Google for the three corpuses.  
\begin{table}
\begin{tabular}{c c} \hline \hline \(\kappa\) & **Interpretation** \\ \hline \(<\)0 & Poor agreement \\
0.01–0.20 & Slight agreement \\
0.21–0.40 & Fair agreement \\
0.41–0.60 & Moderate agreement \\
0.61–0.80 & Substantial agreement \\
0.81–1.00 & Almost perfect agreement \\ \hline \hline \end{tabular}
\end{table}
Table 14: \(\kappa\) interpretation table of agreement.  
As future work, alternative approaches will be explored to select the input web page corpus for the KRS construction, that is, to select the most relevant topic's documents as well as to extend the evaluation to broader topics.  
Conceptualization, J.H. and H.M.M.-C.; Data curation, J.H.; Formal analysis, H.M.M.-C.; Investigation, [PERSON] and H.M.M.-C.; Methodology, [PERSON], H.M.M.-C. and M.M.-S.; Project administration, H.M.M.-C. and M.M.-S.; Software, [PERSON]; Supervision, H.M.M.-C. and M.M.-S.; Validation, J.H., H.M.M.-C. and M.M.-S.; Visualization, H.M.M.-C. and M.M.-S.; Writing--original draft, [PERSON]; Writing--review & editing, [PERSON], H.M.M.-C. and M.M.-S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by \"Fondo Sectorial de Investigacion para la Educacion\", CB SEP-CONACyT Mexico, project number 281565.  
The authors declare no conflict of interest.