Hyperelastic-Based Adaptive Dynamics Methodology in Knowledge Acquisition for Computational Intelligence on Ontology Engineering of Evolving Folksonomy Driven Environment

Due to the rapid growth of structured/unstructured and user-generated data (e.g., social media sites) volume of data is becoming too big or it moves too fast or it exceeds current processing capacity and so traditional data processing applications are inadequate. Computational Intelligence with Concept-based approaches can detect sentiments analyzing the concept based on text expressions without analyzing the singlef words as in the purely syntactical techniques. On human-centric intelligent systems Semantic networks can simulate the human complex frames in a reasoning process providing efficient association and inference mechanisms, while ontology can be used to fill the gap between human and Computational Intelligence for a task domain. For an evolving environment it is necessary to understand what knowledge is required for a task domain with an adaptive ontology matching. To reflect the evolving knowledge this paper considers ontologies based on folksonomies according to a new concept structure called “Folksodriven” to represent folksonomies. To solve the problems inherent an uncontrolled vocabulary of the folksonomy it is presented a Folksodriven Structure Network (FSN): a folksonomy tags suggestions built from the relations among the Folksodriven tags (FD tags). It was observed that the properties of the FSN depend mainly on the nature, distribution, size and the quality of the reinforcing FD tags. So, the studies on the transformational regulation of the FD tags are regarded to be important for an adaptive folksonomies classifications in an evolving environment used by Intelligent Systems to represent the knowledge sharing. The chapter starts from the discussion on the deformation exhibiting linear behavior on FSN based on folksonomy tags chosen by different user on web site resources. Then it’s formulated a constitutive law on FSN investigating towards a systematic mathematical analysis on stress analysis and equations of motion for an evolving ontology matching on an environment defined by the users’ folksonomy choice. The adaptive ontology matching and the elastodynamics are merged to obtain what we can call the elasto-adaptive-dynamics methodology of the FSN. Furthermore it is shown the last development defining a hyperelastic dynamic considering the internal folksonomy behavior of the stress and strain from original to deformed configuration.