  In this paper I present a practical approach for coupling machine learning
(ML) algorithms with knowledge bases (KB) ontology formalism. The lack of
availability of prior knowledge in dynamic scenarios is without doubt a major
barrier for scalable machine intelligence. My view of the interaction between
the two tiers intelligence is based on the idea that when knowledge is not
readily available at the knowledge base tier, more knowledge can be extracted
from the other tier, which has access to trained models from machine learning
algorithms. To analyse this hypothesis, I create two experiments based on
different datasets, which are related directly to risk-awareness of autonomous
systems, analysed by different machine learning algorithms (namely; multi-layer
feedforward backpropagation, Naive Bayes, and J48 decision tree). My analysis
shows that the two-tiers intelligence approach for coupling ML and KB is
computationally valid and the time complexity of the algorithms during the
robot mission is linear with the size of the data and knowledge.
