Blog:Session 2 Ontology Use in Machine Learning

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(Abstract)
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'''Motivation''': Discuss the kinds of ontologies needed for ML, and their "ground rules" and requirements.
 
'''Motivation''': Discuss the kinds of ontologies needed for ML, and their "ground rules" and requirements.
  
Please also see the [[http://ontologforum.org/index.php/Blog:Improving_Machine_Learning_using_Background_Knowledge Track's blog page]].
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Please also see the [http://ontologforum.org/index.php/Blog:Improving_Machine_Learning_using_Background_Knowledge Track's blog page].
  
 
== Agenda and Speakers ==
 
== Agenda and Speakers ==

Revision as of 15:44, 14 March 2017

Contents

Session 2, Track B: Improving Machine Learning Using Background Knowledge

SessionChairs: Mike Bennett and Andrea Westerinen

Abstract

Context: Background knowledge and ontologies can be used to improve machine learning model selection, processing, data preparation and results.

Perception: Machine Learning requires knowledge of the domain being examined to define relevant hypotheses, infer missing information in data, choose relevant features, etc.

Motivation: Discuss the kinds of ontologies needed for ML, and their "ground rules" and requirements.

Please also see the Track's blog page.

Agenda and Speakers

  • Introduction: Mike Bennett and Andrea Westerinen (Slides TBA)

Speakers

  • TBD

Title, Slides and Abstract: TBD

  • Ken Baclawki (College of Computer and Information Science, Northeastern University)

Title, Slides and Abstract: Combining Ontologies and Machine Learning to Improve Decision Making

Decision making is fundamental for modern systems whether they are controlled by humans, computers or both. Machine Learning (ML) is an increasingly popular technique for decision making. This talk will give an overview of work at Oracle and Northeastern University that combines ontologies with ML and other techniques. Benefits of our combination include improving the quality of decisions, making decisions understandable, and improving the adaptability of decision making processes in response to changing conditions. Ontologies are especially well suited to improving decision making that includes both humans and computers. This is the case not only when humans are directly involved as system operators but also when humans are acting as regulators for autonomous systems. These techniques have been applied or proposed in a variety of domains such as customer support, healthcare, cloud services, aircraft operation, and nuclear power plants.

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