# Ontology-Based Methodology for Knowledge Acquisition from Groupware
## 2 Literature Survey
### Ontology in Knowledge Representation  
[PERSON] and [PERSON] [24] presented an ontology web language (OWL) ontology that stores knowledge about cognitive functions and techniques for assessing them. This included knowledge about the qualitative features of psychometric tests and the collection of data, including instances of a screening tests. The ontology can also be utilised to determine the link between cognitive functions and brain activity patterns. Building a consistent knowledge model in the field of cognitive functions assessment, classifying ontology instances, and detecting implicit relationships between class instances are all difficulties that OWL ontologies address. [PERSON] et al. [25] suggested an ontology-based representation of urban heat island mitigation strategies (UHIMSs), focusing on the link between mitigation approaches, performance measures, and urban environments. The conceptualization of terminologies, the formation of linkages, and the integration are the three phases that make up our representation. [PERSON] and [PERSON] [26] proposed a method of representing knowledge using ontology concepts. It uses a bond-graph model to generate an equipment function structure that is related to fault propagation at the part component level. The combination of OWL and resource description framework (RDF [27]) are used to transform human words into a computer-readable representation. Parsing analysis, semantic interpretation, and knowledge representation are the phases in the methodology. [PERSON] [28] suggested the use of natural language processing (NLP) to extract relevant information from semi-structured and structured heterogeneous documents, then used RDF to represent the extracted information in a homogeneous and machine-understandable format, and then mapped the RDF triples to the appropriate concepts in disaster management domain ontologies. [PERSON] et al. [29] presented an ontology-based system for storing cultural information that may be used to manage and adapt a robot's interaction to the user's habits and preferences. This framework is based on three components namely: (i) relevant concepts, individual-specific, and preferences; (ii) program for individual-specific knowledge; and (iii) computational network for acquiring the individual-specific propagating knowledge. In [PERSON] et al. [30], an ontology-based framework to share knowledge between humans and robots was proposed. This framework consisted of an environment for knowledge standardization, sensory module, and evaluation-based analysis for objects situation. [PERSON] et al. [31] proposed an ontology to represent the knowledge of educational assistance in non-communicable chronic diseases (NCDs). Its goal is to assist educational formalities and systems that are designed for preventing and monitoring NCDs. The ontology is specified via competence questions, Semantic Web Rule Language (SWRL) rules, and Semantic Protocol and RDF Query Language (SPARQL) and is implemented in Protege 5.5.0 using OWL. There are 138 classes, 31 relations, 6 semantic rules, and 575 axioms in the current version of the ontology. Although all these studies have pointed towards representing knowledge using ontologies, there has been no indication of representing on-the-job knowledge and practices of virtual software development teams from groupware. For groupware systems, [PERSON] et al. [32] suggested an ontology to formally define context. Physical, organizational, and interaction contexts are the three basic categories of context information. They also showed how this ontology may be used for context inference, offering tools for user communication that are based on the current context of each user. The definition of classes, properties, and instances of these classes formalized a domain. They utilised Protege 3.11 to change the ontology and axioms and the Java Embedded Object Production System (JEOPS) inference machine to construct the rules for context reasoning. [PERSON] et al.