# The Impact of the Human Factor on Communication During a Collision Situation in Maritime Navigation
## 2 Materials and Methods
### Data Validation  
In the model transforming natural language to ontology and ontology to natural language, a two-level method of measuring the accuracy of the proposed model was adopted:  
* The first level includes checking the accuracy of the transformation at the level of single ontological items to natural-language sentences or at the level of single natural-language sentences to ontology;
* The second level checks the accuracy of the transformation of all maritime communication scenarios from ontology to natural language and from natural language to ontology.  
To measure the quality, universally recognized qualitative coefficients [31] based on the uncertainty matrix [32] used in classification [33] studies were adopted. Due to the necessity for accuracy measurement, the division of learning sets and test sets was carried out. The datasets for the first level, considering single natural-language sentences or single ontology items, were smaller. The dataset for the second level of entire scenarios was significantly bigger than the previously mentioned set. For both types of scenarios, the same division of sets was adopted, allowing for the accuracy to be estimated objectively and reliably. The accuracy of the measurements for the two levels was determined using a confusion matrix, as presented in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline
**Predicted Data/Actual Data** & **1** & **2** \\ \hline
1 & TP & TN \\
2 & FN & FP \\ \hline \end{tabular}
\end{table}
Table 4: The confusion matrix for accuracy measurements.  
Figure 3: New conversation model.  
The accuracies of the methods for both types of transformation were verified for the first and second level using the following coefficients:  
\[PPV=\frac{TP}{TP+FP} \tag{2}\]  
where  
PPV--positive predictive value;  
TP--true-positive rate;  
FP--false-positive rate.  
\[NPV=\frac{TN}{TN+FN} \tag{3}\]  
where  
NPV--negative predictive value;  
TN--true-negative rate;  
FN--true-positive rate.  
\[SE=\frac{TP}{TP+FN} \tag{4}\]  
where  
SE--sensitivity rate.  
\[SP=\frac{TN}{TN+FP} \tag{5}\]  
where  
SP--specificity rate.  
\[ACC=\frac{TP+TN}{TP+TN+FP+FN} \tag{6}\]  
where  
ACC--total accuracy rate.  
\[ERR=\frac{FP+FN}{FP+FN+TP+TN} \tag{7}\]  
where  
ERR--total error rate.  
\[F=\frac{(\beta^{2}+1)\cdot P\cdot TP}{(\beta^{2}\cdot P)+TP} \tag{8}\]  
\[\beta=\frac{FN}{TP+FN} \tag{9}\]  
where  
F--F-measure;  
\(\beta\) = FN/(TP + FN)--beta false-positive rate;  
P--sensitivity rate.  
\[FOM=\frac{W\cdot L_{PP}}{(W+1)\cdot L_{\text{JWP}}}+\frac{L_{PN}}{(W+1)\cdot L _{\text{JW}}} \tag{10}\]where  
FOM--prediction quality rate;  
W--ratio of the cost of negative predictions to the cost of positive predictions;  
LPP--number of correct positive predictions;  
LWP--number of all positive predictions;  
LPN--number of correct negative predictions;  
LWN--number of all negative predictions.  
The results of the estimation of the ontology transformation into natural language using a learning curve is presented below (Figure 4).  
The input data for the method are shown in the relevant tables in Section 3.