The technological world has grown by incorporating billions of small sensing
devices, collecting and sharing huge amounts of diversified data. As the number of
such devices grows, it becomes increasingly difficult to manage all these new data
sources. Currently there is no uniform way to represent, share, and understand
IoT data, leading to information silos that hinder the realization of complex
IoT/M2M scenarios. IoT/M2M scenarios will only achieve their full potential
when the devices work and learn together with minimal human intervention. In
this paper we discuss the limitations of current storage and analytical solutions,
point the advantages of semantic approaches for context organization and extend
our unsupervised model to learn word categories automatically. Our solution was
evaluated against Miller-Charles dataset and a IoT semantic dataset extracted
from a popular IoT platform, achieving a correlation of 0.63.