# Ontology-Based Methodology for Knowledge Acquisition from Groupware
## 2 Literature Survey
### Ontology Equality of Concepts  
The notion of equality of concepts is necessary for the recognition of new concepts, when comparing with an existing ontology. This will be a major contribution in this research. [PERSON] et al. [42] introduced a method for validating the addition of a new concept to an ontology. The method functions in three stages, by firstly locating the neighborhood of the concept (C) within the basic ontology (Ob) and storing their semantic-similarity values in a stack. The neighborhood denotes concepts in Ob that are more similar to C. Secondly, the semantic similarity between C and its neighborhood that was discovered in the first stage was evaluated in the general ontology (Og). Finally, the correlation between the values that were noted in the previous stage was assessed. The authors used the whole WordNet as a general ontology and a WordNet branch as a basic ontology. They utilised the edge semantic similarity measure. To establish equivalence relations among concepts, [PERSON] et al. [43] proposed a new approach that was based on Classification with Word and CONtext-Similarity (CWCONS). The core idea behind CWCONS is to categorize ontology tree nodes into two types: classification nodes and concept nodes, both of which rely on the ontology's tree structure. To analyse similarities, they employed the longest common substring (LCS) and [PERSON]'s similarity model. [PERSON] et al. [44] proposed a similarity measure to calculate the similarity value of two ontology entities/concepts, after which an optimal model for the ontology matching problem is built; then, an evolutionary algorithm-based fully automated matcher is provided to solve the ontology matching issues; and finally, to balance the workload on user and the impact of his activity, concept hierarchy graph-based reasoning methodologies were proposed. [PERSON] and [PERSON] [45] suggested ontology matching algorithms that are capable of locating compound mappings across diverse biomedical ontologies. This is akin to ternary mappings, for example, asserting that \"aortic valve stenosis\" (HP:0001650) is comparable to the intersection of an \"aortic valve\" (FMA:7236) and is \"constricted\" (PATO:0001847). To cope with the higher computing demands, the algorithms used search space filtering that was based on partial mappings between ontology pairings. The evaluation of the algorithms was done with precision. [PERSON] and [PERSON] [46] presented a granular computing approach for mapping numerous existing ontologies into a single representative domain ontology. It is made up of four granular computing processes: association,isolation, purification, and reduction, which can be used to unify a set of related nodes in ontologies. The approach accomplishes ontology mappings by going through two phases: similarity calculation and granular computing. The evaluation was based on ontologies for transportation and vehicles. To improve the generalization performance of a mapping between two ontologies, [PERSON] et al. [47] proposed HISDOM, a novel ontology mapping system. HISDOM compares ontologies that are based on a variety of characteristics such as concept names, attributes, instances, and structural similarities. It calculates ontology mapping similarity using a convolutional neural network. The Ontology Alignment Evaluation Initiative (OAEI) dataset was used in HISDOM's experiments. [PERSON] [48] proposed a method for ensuring that the two ontologies remain aligned in the face of reconciliation restrictions that were defined between them. These restrictions deal with semantic linkages which help for better grasping of overlapping in different perspectives. The technique extension is provided from the user's perspective before being theoretically formalized to achieve the goal of establishing a solid basis for comprehending limits and restrictions in concrete applications. [PERSON] and [PERSON] [49] proposed combining domain-specific ontologies that were based on multiple external semantic resources to address the semantic heterogeneity challenge.