# Ontology-Based Methodology for Knowledge Acquisition from Groupware
## 5 Discussion  
This study presented a technique for the identification and hooking of concepts from sentences into an existing ontology. It provides a novel approach for on-the-job expertise knowledge in groupware to be harvested and represented in ontology to support the development of service robots and intelligent agents. In terms of evaluation, the F1 score of 0.97 means that the technique has higher precision and recall and is very much in line with  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{3}{c}{**Grand Ontology 1**} & \multicolumn{3}{c}{**Grand Ontology 2**} \\ \cline{2-7}  & **ER** & **ACY** & **F1** & **ER** & **ACY** & **F1** \\ \hline Concept Naming & 0.048 & 0.952 & 0.963 & 0.050 & 0.949 & 0.984 \\ Hooking locations & 0.052 & 0.948 & 0.960 & 0.053 & 0.947 & 0.957 \\ Mean & 0.05 & 0.95 & 0.96 & 0.05 & 0.95 & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The computed of the confusion matrix.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Matrix Variables**} & \multicolumn{3}{c}{**Grand Ontology 1**} & \multicolumn{3}{c}{**Grand Ontology 2**} \\ \cline{2-5}  & **Concept** & **Hooking** & **Concept** & **Hooking** & **Hooking** & **Location** & **Total** \\  & **Naming** & **Location** & **Naming** & **Location** & **Location** & \\ \hline True-positive & 457 & 456 & 213 & 214 & 1340 \\ False-positive & 32 & 34 & 15 & 14 & 95 \\ True-negative & 232 & 231 & 109 & 106 & 678 \\ False-negative & 3 & 4 & 2 & 4 & 13 \\ Total & 724 & 725 & 339 & 338 & 2126 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of classification by the evaluators.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Matrix Variable**} & \multirow{2}{*}{**Thresholds**} & \multirow{2}{*}{**This Research**} & \multirow{2}{*}{**[PERSON] et al. [43]**} & **[PERSON] and [PERSON] [44]** & **[PERSON] and [PERSON] [48]** \\ \hline F1 score & greater than 0.90 & 0.97 & 0.81 & 0.96 & 1.0 \\ Accuracy & greater than 0.90 & 0.95 & - & - & - \\ Error rate & less than 0.10 & 0.05 & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative results between this research and existing studies.  
the universally recommended thresholds. This suggests that our technique can be used to update an existing ontology with current trends and innovations coming from sentences. This, in turn, will provide significant background for AI development and deployment. The results further indicate that the error rate (0.05) and accuracy rate (0.95) are well aligned to the suggestions of the universal thresholds (less than 0.10 and 0.90, respectively). This is attributed to the accurate naming and hooking of the harvested new concepts vis-a-vis the destination ontology. Comparison with other existing works also holds that the proposed technique can be deemed to be a success. This is based on the comparative analysis of the F1 scores, which shows that the proposed technique is significantly better than [PERSON] et al. [44], similarly for [PERSON] and [PERSON] [45], but performs slightly better than [PERSON] and [PERSON] [49].  
Getting recent literature with the same evaluation scope to perform a comparative analysis is rather challenging, and, as such, this was based on the F1-scores only. Given the situation, the error rate and accuracy rate were compared only with the threshold, and not yet with other existing works. As literature is scant on mapping concepts from sentences into an existing ontology, comparisons were made with results of automated mapping and merging of concepts between ontologies even though ours were carried out manually in this study. In the future, we plan to fully automate the processes in this technique and then re-evaluate with more comparable balanced matrix variables.