# Ontology-Based Methodology for Knowledge Acquisition from Groupware
## 6 Conclusions & Future Work  
In this paper, we presented a novel ontology-based framework for knowledge acquisition from sentences (text) in groupware, as well as a technique for the identification and hooking/insertion of new concepts into an existing ontology. The framework is currently semi-automated and has five main components, to take in textual data, analyze it, and to update an existing ontology that can be utilized to power intelligent agents. Ontology plays a focal role to provide a communication framework that facilitates the definition of common vocabularies in AI applications. However, most of the previous research efforts in this domain are similar in that all are looking for new knowledge to add into a destination ontology from another existing ontology. Within such efforts, there is no clear consensus on the notion of equality, similarity, and equivalence of concepts, which is a necessity for the recognition of new concepts from any given source to be compared with an existing ontology. In addition, the literature is indeed scant on a formalized technique for the identification and insertion/hooking of a new concept into an existing ontology from other existing ontologies, let alone when the source is from sentences (free text), and especially from groupware. The novelty of our approach is thus the discovery of new concepts from sentences (in groupware) using a proposed FEA approach for the recognition of new concepts and the insertion/hooking of the new concepts into an existing ontology. In terms of evaluation, the F1 score of 0.97 means that the technique has higher precision and recall which is very much in line with the universal recommendations for the threshold. The results further indicate that the error rate (0.05) and accuracy rate (0.95) are also aligned with the suggestions for the universal thresholds (less than 0.10 and 0.90, respectively). 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 that show that the proposed technique is slightly better than those that are available in the literature. 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 it was done manually in this study. In the future, we plan to fully automate the framework and all its processes and then re-evaluate with more comparable balanced matrix variables, most especially using datasets from electronic clinical records and agricultural practices domains. We also plan to incorporate the acquisition of tacit knowledge from groupware. **Author Contributions:** Conceptualization, C.F.U. ; Methodology, C.F.U. ; Supervision, Y.L., Z.Y. and T.M.C. ; Writing--original draft, C.F.U. ; Writing--review and editing, Y.L. and Z.Y. All authors have read and agreed to the published version of the manuscript. **Funding:** Sunway University Postgraduate Research Support. This research received no external funding. **Institutional Review Board Statement:** The study was conducted according to the guidelines of Sunway University, Malaysia. **Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest. **Abbreviations**  
The abbreviations used in this paper are as follows:  
**List of Abbreviations**  
AH  
AI  
CA  
CA  
CA  
CA  
CA  
CA  
CA  
CA  
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