A social network extraction based on relation analysis

Social network services, such as Facebook and Twitter, have recently received great attention. Previous research on social networks has focused on its architecture, that is, the existence of a path between persons in the network. Many studies have analyzed the factors affecting the architecture of the social network. However, studies on the semantic association in the network are rare. Extracting various types of relationships among people that exist in a real society is necessary for future business and social studies. We propose an ontology-based social network extraction method based on inference. A new method to compute the strength of a association among entities is suggested. Using the proposed method, a new social network is extracted. Empirical experiments are performed to apply the proposed method. The new method can be used to extract a social network from associations among entities in an ontology-based knowledge base.