Blog:Improving Machine Learning using Background Knowledge

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(References)
(Organized by Mike Bennett and Andrea Westerinen)
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   * Design/construction/content/evolution/... requirements for an ontology to support machine learning
 
   * Design/construction/content/evolution/... requirements for an ontology to support machine learning
  
We explore the problem space in the first Track B session on March 15, by way of a targeted presentation on learning for decision support and a use case from the financial space. We want to start a conversation on the kinds of ontologies needed for ML, and their "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? These issues will set the scene for the second Track B session on April 12.
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We explore the problem space in the first Track B session on March 15, by way of a targeted presentation on learning for decision support and (to-be-confirmed) a presentation on improving understandability of ML results. We want to start a conversation on the kinds of ontologies needed for ML, and their "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? These issues will set the scene for the second Track B session on April 12.
  
 
== References ==
 
== References ==

Revision as of 00:47, 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, 2]. Also, there is a bewildering array of model choices and combinations. Background knowledge could improve the quality of ML results by using reasoning techniques to select learning models and prepare the training and examined data [3] (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 explore the problem space in the first Track B session on March 15, by way of a targeted presentation on learning for decision support and (to-be-confirmed) a presentation on improving understandability of ML results. We want to start a conversation on the kinds of ontologies needed for ML, and their "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? These issues will set the scene for the second Track B session on April 12.

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.

[2] Falk, Courtney, and Stuart, Lauren. "Meaning-based machine learning for information assurance". Retrieved from http://www.sciencedirect.com/science/article/pii/S2352664516300207.

[3] Domingos, Pedro. "A Few Useful Things to Know about Machine Learning". Retrieved from https://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf.

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