Abstract
Nowadays, the process of ontology learning for describing heterogeneous systems is an influential phenomenon to enhance the effectiveness of such systems using Social Network representation and Analysis (SNA). This paper presents a novel scenario for constructing adaptive architecture to develop community performance for heterogeneous communities as a case study. The crawling of the semantic webs is a new approach to create a huge data repository for classifying these communities. The architecture of the proposed system involves two cascading modules in achieving the ontology data, which is represented in Resource Description Framework (RDF) format. The proposed system improves the enhancement of these environments achieving both semantic web and SNA tools. Its contribution clearly appears on the community productions and skills developments. Python 3.9.0 platform was used for data pre-processing, feature extraction and clustering via Naïve Bayesian and support vector machine. Two case studies were conducted to test the accuracy rate of the proposed system. The accuracy rate for the case studies was (90.771%) and (90.1149 %) respectively, which is considered an affective precision when it is compared with the related scenario with the same data set.
Highlights
Ontology learning is a suitable mean for constructing a useful scenario to understand and analyse a social network [1]
The results depicted were achieved after classifying different items in homogeneous environments via supervised learning approach with labelled samples for discriminating between two or more classes using Support vector machine (SVM) technique
This paper presents a novel scenario for constructing adaptive architecture to develop community performance using ontologies learning via Social Network representation and Analysis (SNA) with two clustering algorithms
Summary
Figures (1-4) represents the various classification techniques used as classifiers and Figure 5 depicts the layout of the proposed system. They demonstrate how this mix will be used to represent people's preferences and figure out whether writers with shared goals interact through using R mailing lists R-help and R-devel. Chin et al 2007 [12] defined a tool for identifying bloggers' groups that combines a sense of group assessment and social network analysis (SNA) This concept was applied to a blog about European online music. Mislove et al [14] presents a large-scale estimation research and interpretation of the function of several online communities They examined data from Flickr, YouTube, Live Journal, and Orkut, four prominent online social networks.
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