# Ontology-Based Methodology for Knowledge Acquisition from Groupware
###### Abstract  
Groupware exist, and they contain expertise knowledge (explicit and tacit) that is primarily for solving problems, and it is collected on-the-job through virtual teams; such knowledge should be harvested. A system to acquire on-the-job knowledge of experts from groupware in view of the enrichment of intelligent agents has become one of the important technologies that is very much in demand in the field of knowledge technology, especially in this era of textual data explosion including due to the ever-increasing remote work culture. Before acquiring new knowledge from sentences in groupware into an existing ontology, it is vital to process the groupware discussions to recognise concepts (especially new ones), as well as to find the appropriate mappings between the said concepts and the destination ontology. There are several mapping procedures in the literature, but these have been formulated on the basis of mapping two or more independent ontologies using concept-similarities and it requires a significant amount of computation. With the goal of lowering computational complexities, identification difficulties, and complications of insertion (hooking) of a concept into an existing ontology, this paper proposes: (1) an ontology-based framework with changeable modules to harvest knowledge from groupware discussions; and (2) a facts enrichment approach (FEA) for the identification of new concepts and the insertion/hooking of new concepts from sentences into an existing ontology. This takes into consideration the notions of equality, similarity, and equivalence of concepts. This unique approach can be implemented on any platform of choice using current or newly constructed modules that can be constantly revised with enhanced sophistication or extensions. In general, textual data is taken and analysed in view of the creation of an ontology that can be utilised to power intelligent agents. The complete architecture of the framework is provided and the evaluation of the results reveal that the proposed methodology performs significantly better compared to the universally recommended thresholds as well as the existing works. Our technique shows a notable high improvement on the F1 score that measures precision and recall. In terms of future work, the study recommends the development of algorithms to fully automate the framework as well as for harvesting tacit knowledge from groupware. k 2022120220220220202  
agents is the know-how about the domain in which they are to be used [3]. Virtual teams' groupware contains knowledge and practices regarding their specific fields, but in a format (audio, video, and free text) that intelligent agents cannot understand directly. This on-the-job expertise knowledge in groupware is readily available and should be harvested and represented in some ontology to support the development of service robots and intelligent agents, amongst others. Groupware has been defined differently by various scholars [4], but in all of the definitions, being computer-based and used as a collaboration tool are the common traits. As such, this study defines groupware as a computer-enabled distributed environment that facilitates cooperation and coordination among a group of individuals that are working toward a common goal. It is mostly developed to make knowledge creation and sharing faster and easier in most organizations [5]. It also facilitates explicit and tacit knowledge sharing in the organizations, according to [PERSON] [6]. Due to the benefits that they provide, groupware sites have exploded in popularity recently. Every day, a new groupware application is created and launched, each with its own set of new features, flexibility, and good usability. SourceForge, MicroExchange, Huddle, Fuze, and Drupal are just a few examples of groupware systems that have been tested to handle high communication volumes over time [7,8].