## 4 Knowledge Graph Evaluation  
Despite the validation step during the information retrieval process, the graph may contain syntax and semantic issues that impact its quality. A low-quality KG produces low-quality applications; therefore, an exhaustive evaluation becomes a necessary task to build high-quality KGs [62; 63; 64].  
In [64], the authors highlight 'accuracy', 'consistency', 'timeliness', 'completeness', 'trustworthiness', and 'availability' as the main evaluation dimensions of the KG quality, where 'accuracy' is associated to error detection in relations, entity types, and attributes, 'timeliness' refers to global update and local update of KG, and 'completeness' means completion of relations, entity types, and attributes. Nonetheless, in our work, and based on the research [62], 18 requirements are considered when assessing the quality of a KG (see Table 2).  
Table 2 shows a summary of the KG quality requirements and Figure 8 presents the classification of these quality requirements into four high-level dimensions. Table 3 gathers the requirement numbers to be evaluated in the KG as identified in Table 2 and Figure 8;  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
1. & Triples should be concise & 8. & Data for constructing a knowledge graph should be different types and from different resources & 13. & Knowledge graph should be publicly available and proprietary \\
2. & Contextual information of entities should be captured & 8. & & 14. & Knowledge graph should be authority \\
3. & Knowledge graph does not contain redundant triples & 9. & Synonyms should be mapped and ambiguities should be eliminated to ensure reconcilable expressions & 15. & Knowledge graph should be concentrated \\
4. & Knowledge graph can be updated dynamically & 10. & Knowledge graph should be organized in structured triples for easy processing by machine & 16. & The triples should not contradict with each other \\
5. & Entities should be densely connected & 11. & The scalability with respect to the KG size & 17. & For domain specific tasks, the knowledge graph should be related to that field \\
6. & Relations among different types of entities should be included & 12. & The attributes of the entities should not be missed & 18. & Knowledge graph should contain the latest resources to guarantee freshness \\
7. & Data source should be multi-field & 12. & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Requirements of KG quality.  
Figure 7: New instance displayed in OntoGraf.  
each cell that is filled with light grey indicates a positive response to its fulfillment; on the other hand, dark grey designates the non-compliance with such quality requirement.  
\begin{table}
\begin{tabular}{|c c c c|c|} \hline
1 & 2 & 3 & 4 & 5 \\ \hline
6 & 7 & 8 & 9 & 10 \\ \hline
11 & 12 & 13 & 14 & 15 \\ \hline
16 & 17 & 18 & & \\ \hline \end{tabular}
\end{table}
Table 3: Evaluation results of the graph.  
Figure 8: Dimensions of quality requirements in KGs [62].  
In conclusion, the evaluation showed that the resulting graph is accurate, consistent, timely, complete, reliable, and available as dimensions of the KG quality assessment. However, the points to improve are those related to:  
Quality requirement (5)--Entities must be densely connected: given the automatic nature of the KG population process suggested in this work, not all the attributes and properties associated to each entity are found in the considered data sources. Consequently, the resulting KG is only sparsely connected.  
Quality requirement (9)--Synonyms must be mapped and ambiguities must be removed to ensure reconcilable expressions: Spanish content gathered from heterogeneous sources is prone to containing synonyms and ambiguous terms. It is yet to be conceived an automatic mechanism capable of dealing with such challenge with total effectiveness.