# Ontology-Based Methodology for Knowledge Acquisition from Groupware
## 3 Design  
This section presents the proposed framework (Figure 1), one of the major contributions of this paper. The design is novel in comparison to its forerunners and the closest comparable framework that is accessible, according to literature, is as suggested in [PERSON] et al. [23]. The said framework, event collective knowledge (eCK), is aimed at events extraction from social media and other multimedia digital ecosystem in general, whereas our proposed framework is focused on the acquisition of on-the-job knowledge, procedures, and practices from virtual teams' groupware. The datasets classifiers, uniform data model normalizer, collective knowledge generator, and query formulator are the four processing phases of eCK, which are related to the first three phases in our proposed framework. The five stages of our proposed framework are Groupware-Chamber (GC), Cleansing-Chamber (CC), Harvesting-Hub (HH), Acquisition-Hub (AH), and Knowledge-Hub (KH), which allow textual datasets to be processed from its free (unstructured) state to a refined (structured) knowledge in the form of ontologies for enabling intelligent agents. The AH and KH are included in our proposed framework because they serve as a knowledge acquisition and a repository of ontologies modules, respectively, and these features are not present in the eCK. The framework that is proposed in this paper is semi-automated with reusable and incremental modules that have been formalized.  
In terms of operability, all the components within the framework function collaboratively to ensure that textual data is taken and analyzed in view of the creation of an ontology that can be utilized to power intelligent agents. The input channel for entering text into the framework is the GC. The entered text is cleansed at the CC and dispensed in  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Authors/Year** & \begin{tabular}{c} **Source of New** \\ **Knowledge** \\ \end{tabular} & **Research Focus** & **Sampled Ontology** & **Techniques Used** & **Evaluation** \\ \hline \begin{tabular}{c} [PERSON] and \\ [PERSON] [49] \\ \end{tabular} & Existing ontology & \begin{tabular}{c} Combining two or \\ more existing \\ ontologies \\ \end{tabular} & \begin{tabular}{c} EMET, AGROVOC, \\ and NAL \\ \end{tabular} & OAEI & Recall, Precision \\ \hline \begin{tabular}{c} [PERSON] and \\ [PERSON] [50] \\ \end{tabular} & Existing ontology & \begin{tabular}{c} Matching two or more \\ existing ontologies \\ \end{tabular} & WordNet & Similarity measure & FCA \\ \hline \begin{tabular}{c} [PERSON] and [PERSON] [51] \\ \end{tabular} & Existing ontology & \begin{tabular}{c} Matching two or more \\ existing ontologies \\ \end{tabular} & \begin{tabular}{c} Tourism info and \\ tourists ontologies \\ \end{tabular} & \begin{tabular}{c} FCA-based \\ approaches \\ \end{tabular} & \begin{tabular}{c} FCA and Bayesian \\ analysis \\ \end{tabular} \\ \hline \begin{tabular}{c} This research \\ groupware \\ \end{tabular} & \begin{tabular}{c} Sentences in \\ sentences and \\ inserting/hooking into \\ an existing ontology \\ \end{tabular} & \
\begin{tabular}{c} Software knowledge \\ ontology \\ \end{tabular} & FEA &
\begin{tabular}{c} Error rate, \\ Accuracy, F1 score \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._a format that the HH can understand. At the HH, a variety of strategies are employed to extract knowledge from the cleansed text on the basis of their respective meanings. The AH then assists in validating whether the collected knowledge is an existing one (i.e., it is already in the target ontology) or a new one. This is done via the processes of identification and hooking whereby the recognised new knowledge is placed within the target ontology in the KH. Below are detailed descriptions of how each of these components operates as well as their methodologies and internal structures.