# Attention-Survival Score: A Metric to Choose Better Keywords and Improve Visibility of Information
## 11 Experimental Design
### Theoretical Model Validation
#### 11.1.2 Ontology Analysis  
Our purpose was to map the terms in our datasets onto the ontology structure as defined by WordNet and CSO. Before mapping terms, we first wanted to perform an exploratory task on both WordNet and CSO to explore the ontologies' behavior and clarify whether the theoretical process of attention and survival metrics over an ontology was close to our assumptions. WordNet consists of different synsets outside a hierarchy, but we only studied those connected to the root synset (i.e., entity). A synset can have one or more lemmas, so we used the median value of the attention and survival scores across all lemmas. Using the median value instead of the mean to determine the score of a synset is based on the fact that the distribution scores of lemmas tend to present a skewed result. However, we used the mean attention and survival numbers to compute the scores per level. Table 1 shows the distribution of the Scopus and Google values' overall levels of depth in WordNet (\(\alpha\) = 0.5). We only analyzed depth levels that were greater than five, because, for prior levels, the number of synsets was reduced too much, which could lead to incorrect conclusions and inconsistent results. \begin{table}
\begin{tabular}{l c c} \hline \multicolumn{3}{c}{**Scopus and Google Scores in WordNet Ontology**} \\ \hline  & **Level** & **Scopus** & **Google** \\ \hline  
\end{tabular}
\end{table} 
Table 1: Scopus and Google scores per depth level in WordNet. All values are the average scores of each synset with the same distance to the root node, and the value of each synset was computed by considering the median value of all lemmas within the synset. Note that level 1 only has the root synset. After computing the attention and survival scores and extracting the mean values per level, we noted that the scores were on different scales, so we needed to perform a min-max normalization to keep all values in the same range [0, 1]. We did this to make the analysis of the effect of both scores in the ontologies more accessible. Figure 4 shows the survival and attention score evolution across the WordNet structure (\(\alpha=0.5\)). As we can see, survival starts close to 1.0 at depth 17 and is in a continuous decline until being surpassed by attention at level 6, where the equilibrium point is located. Meanwhile, attention continually grows until it reaches its maximum value at level 2. The equilibrium point is located at the coordinates (6.38, 0.48), that is, in level 6, producing an AS score of 0.48, while the maximum score is achieved at level 2, with an AS score of 0.62. If we check the results from Table 1, we can see that the average number of articles per level is significantly reduced until level 7, while attention tends to grow uniformly. The observed behavior over the WordNet ontology is in consonance with our proposals. Moreover, we have empirically corroborated that more specificity is related to low attention and high survival scores. Concerning the WordNet dynamic, we can see how most depth levels contain very specific terms, which are quite unattractive according to Google searches and the number of articles retrieved by a Scopus search. From these results, one should not deduce that the best option is to choose keywords from levels 7 or 2. WordNet is a generic ontology that contains many terms that are not common in the academic field. Therefore, a domain-specific ontology would be a better option for choosing keywords. A good approach could be to choose an ontology according to the criteria described by [33] (for example, the authors mention clarity, consistency, conciseness, expandability, correctness, completeness, minimal ontological commitment, and minimal encoding bias, among other criteria). Our purpose in employing WordNet was to use a generic and widely validated ontology to analyze the distribution of attention and survival scores. The case for CSO is illustrated in Figure 5.  
Figure 4: Evolution of attention, survival, and attention–survival score (AS) throughout the WordNet structure.