Multi-Layered Ontology Models for Dynamic Knowledge Representation in Social Network Analysis

In the rapidly evolving landscape of social networks, understanding the multifaceted nature of human interactions and the dissemination of information is pivotal. This paper introduces a novel approach to dynamic knowledge representation through the development of Multi-Layered Ontology Models tailored for Social Network Analysis (SNA). The core of this research lies in architecting a robust, scalable, and semantically rich framework that leverages the principles of data mining and web technologies to dissect and interpret the complex structure of social networks. The ontology layers are intricately designed to represent various dimensions of social interactions, including temporal dynamics, user behavior, and network evolution. Each layer is interlinked yet distinct, providing a granular view of the social fabric and enabling precise inference and prediction of patterns. The methodology adopts an innovative blend of semantic web technologies for ontology construction, coupled with advanced data mining techniques to efficiently process and analyze large volumes of social data. The result is a dynamic model that adapts to the ever-changing social context, offering valuable insights into trends, community structures, and influence dissemination. The implications of this research are vast, impacting areas ranging from targeted marketing to public opinion analysis. This paper sets a new precedent for interdisciplinary research in SNA, opening avenues for further exploration and refinement.