# Ontology-Based Methodology for Knowledge Acquisition from Groupware
###### Abstract
In AI, or simply intelligent agents, ontology plays a focal role to provide a communication framework that facilitates the definition of common vocabularies for applications and other independent semantic functions [9]. As a conceptualization in an explicit specification, intelligent agents need to have some knowledge about the domain in which they operate, and the knowledge is to be represented accurately in them. This has been instantiated in chatbots, question and answering systems, knowledge graphs, decision support, and expert systems [10]. Owing to the benefits that remote work culture has offered [11], on-the-job knowledge and practices of experts can be harvested and represented in the form of an ontology to enhance the development of AI products. Several empirical studies that have been conducted recently have revealed difficulties in processing and acquiring new knowledge from unstructured text [12, 13, 14, 15]. This is especially evident from groupware discussions, considering the nature of conversations which are not written in formal sentence patterns and contain grammatical and typographical errors. There are also problems in finding the mappings between the harvested knowledge and the appropriate locations in the destination ontology or existing ontology since the concept is coming from sentences and has less information [16]. A few studies have indeed presented mapping procedures [17], matching [18, 19, 20], and alignment [21, 22], but these studies are formulated based on combining two or more independent ontologies, and mostly using concept similarities that require a significant amount of computation and calculations. These are ontology-to-ontology specific, hence the literature remains scant on a technique for the identification and insertion (hooking) of new concepts from sentences into an existing ontology. Within the context of this study, concept identification represents the process of new knowledge recognition while the insertion or hooking describes the procedure for adding the newly recognised knowledge into an existing ontology. Considering a precondition for hooking, the recognition of new knowledge is a core step needed with regard to adding new concepts into an existing ontology. This is to ensure that all the distinct elements in the ontology completely comprehend one another without concept duplications and inconsistencies. Furthermore, the representation nature of concepts in ontologies has led to having different concept hierarchies for equal, similar, or equivalent concepts in the same ontology, more especially when a new concept has less information, no attribute and values, and relations in/out such as being harvested from sentences. The main contributions of this paper are to propose the following:  
1. An ontology-based framework with changeable modules to harvest knowledge from groupware discussions. The uniqueness of this framework lies in its five processing phases and components; earlier closet framework [23] focuses on event extraction with four processing phases, which are covered by the first three phases in our proposed framework. This novelty of our framework is the inclusion of an acquisition hub and a knowledge chamber, which are not present in earlier frameworks. 2. A facts enrichment approach (FEA) for the identification and hooking of new concepts from sentences into an existing ontology, taking into consideration the notions of equality, similarity, and equivalence of concepts. The novelty of the FEA lies within its ability to identify and insert/hook a concept with less information such as those coming from sentences into an existing ontology. The remaining part of this paper is organized as follows: Section 2 examines the current research trends in this domain; Section 3 introduces the design of the framework; Section 4 presents evaluation approaches, processes, and results; Section 5 provides a discussion of results; and finally, the conclusion and future work are provided in Section 6.