Blog:Improving Machine Learning using Background Knowledge

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Use of Ontologies to Improve Machine Learning Techniques and Results

Organized by Mike Bennett and Andrea Westerinen

Machine Learning (ML) is valuable for exploring large amounts of data. However, it has been noted that if no background knowledge is employed, the results may not be understandable or of sufficient quality. Background knowledge can also improve the quality of machine learning results by using reasoning techniques to select learning models, clean data or improve data selection (reducing large, noisy data sets to manageable, focused ones).

The objective of this Ontology Summit 2017 track is to explore the problem space by way of a couple of use cases in the financial space among others, and to look at possible architectures for using ontologies, vocabularies or other resources in processing natural language text. We will consider the kinds of ontology that would be needed for the example use cases, the challenges in using multiple source ontologies and some possible ground rules for what kinds of ontologies or vocabularies would be needed. 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.


Buitelaar, Paul, Philipp Cimiano, and Bernardo Magnini. "Ontology learning from text: An overview." Ontology learning from text: Methods, evaluation and applications 123 (2005): 3-12.

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