Blog:Improving Machine Learning using Background Knowledge

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(Organized by Mike Bennett and Andrea Westerinen)
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==== Organized by Mike Bennett and Andrea Westerinen ====
 
==== Organized by Mike Bennett and Andrea Westerinen ====
  
Machine Learning (ML) is based on defining and using mathematical models to perform tasks, predict outcomes, make recommendations, etc. Initial models can be specified by a data scientist, and/or constructed through combinations of supervised and unsupervised learning and pattern analysis. However, it has been noted that if no background knowledge is employed, the ML results may not be understandable[1]. Background knowledge can also improve the quality of the results by using reasoning techniques to select learning models, clean data or improve data selection (reducing large, noisy data sets to manageable, focused ones).   
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Machine Learning (ML) is based on defining and using mathematical models to perform tasks, predict outcomes, make recommendations, etc. Initial models can be specified by a data scientist, and/or constructed through combinations of supervised and unsupervised learning and pattern analysis. However, it has been noted that if no background knowledge is employed, the ML results may not be understandable [1]. Also, there is a bewildering array of model choices and combinations. Background knowledge could improve the quality of the results by using reasoning techniques to select learning models and prepare the training and examined data [2] (reducing large, noisy data sets to manageable, focused ones).   
  
 
The objective of this  Ontology Summit 2017 track is to understand:  
 
The objective of this  Ontology Summit 2017 track is to understand:  

Revision as of 00:22, 14 March 2017

Contents

Purpose

Use of Ontologies to Improve Machine Learning Techniques and Results

Organized by Mike Bennett and Andrea Westerinen

Machine Learning (ML) is based on defining and using mathematical models to perform tasks, predict outcomes, make recommendations, etc. Initial models can be specified by a data scientist, and/or constructed through combinations of supervised and unsupervised learning and pattern analysis. However, it has been noted that if no background knowledge is employed, the ML results may not be understandable [1]. Also, there is a bewildering array of model choices and combinations. Background knowledge could improve the quality of the results by using reasoning techniques to select learning models and prepare the training and examined data [2] (reducing large, noisy data sets to manageable, focused ones).

The objective of this Ontology Summit 2017 track is to understand:

 * Challenges in using different kinds of background knowledge in machine learning
 * Role of ontologies, vocabularies and other resources to improve machine learning results
 * Design/construction/content/evolution/... requirements for an ontology to support machine learning

We will explore the problem space by way of targeted presentations and use cases in various domains (such as the financial space). We will consider the kinds of ontologies that would be needed for ML, and possible ground rules and requirements. We will also explore other aspects of an ontology-driven natural language architecture such as the application of semantics to neural learning functionality and the possible role of statistical analysis in this. A recurring question in this and other comparable problem spaces, is where does the human fit in the loop and what do they do? This will set the scene for the second Track B session on April 12 when we hope to have some examples of these architectures put into practice.

References

[1] Guo, Yunsong, and Selman, Bart. "ExOpaque: A Framework to Explain Opaque Machine Learning Models Using Inductive Logic Programming". Retrieved from http://www.cs.cornell.edu/~guoys/publications/ExOpaqueICTAI07.pdf.

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