Automatic Data Deformation Analysis on Evolving Folksonomy Driven Environment

TL;DR

This paper introduces a Folksodriven framework for analyzing evolving folksonomies, enabling adaptive classification and feature extraction to improve predictive modeling in dynamic knowledge-sharing environments.

Contribution

It proposes a novel Folksodriven ontology structure for representing folksonomies and studies its transformational regulation for adaptive classification in evolving environments.

Findings

Effective folksonomy-based feature extraction for machine learning

Enhanced adaptive classification in evolving knowledge environments

Framework supports predictive modeling with dynamic folksonomy data

Abstract

The Folksodriven framework makes it possible for data scientists to define an ontology environment where searching for buried patterns that have some kind of predictive power to build predictive models more effectively. It accomplishes this through an abstractions that isolate parameters of the predictive modeling process searching for patterns and designing the feature set, too. To reflect the evolving knowledge, this paper considers ontologies based on folksonomies according to a new concept structure called "Folksodriven" to represent folksonomies. So, the studies on the transformational regulation of the Folksodriven tags are regarded to be important for adaptive folksonomies classifications in an evolving environment used by Intelligent Systems to represent the knowledge sharing. Folksodriven tags are used to categorize salient data points so they can be fed to a machine-learning system and "featurizing" the data.