Automatic Data Deformation Analysis on Evolving Folksonomy Driven Environment

12/30/2016
by   Massimiliano Dal Mas, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/31/2019

Emergent Behaviors from Folksonomy Driven Interactions

To reflect the evolving knowledge on the Web this paper considers ontolo...
research
04/18/2019

Making Meaning: Semiotics Within Predictive Knowledge Architectures

Within Reinforcement Learning, there is a fledgling approach to conceptu...
research
11/19/2022

Towards Ontology-Based Requirements Engineering for IoT-Supported Well-Being, Aging and Health

Ontologies serve as a one of the formal means to represent and model kno...
research
05/08/2020

Knowledge Patterns

This paper describes a new technique, called "knowledge patterns", for h...
research
09/20/2017

Temporal Pattern Mining from Evolving Networks

Recently, evolving networks are becoming a suitable form to model many r...
research
02/07/2018

An Ontology Based Modeling Framework for Design of Educational Technologies

Despite rapid progress, most of the educational technologies today lack ...
research
07/17/2020

A Review of Meta-level Learning in the Context of Multi-component, Multi-level Evolving Prediction Systems

The exponential growth of volume, variety and velocity of data is raisin...

Please sign up or login with your details

Forgot password? Click here to reset