  The Semantic Web aims at representing knowledge about the real world at web
scale - things, their attributes and relationships among them can be
represented as nodes and edges in an inter-linked semantic graph. In the
presence of noisy data, as is typical of data on the Semantic Web, a software
Agent needs to be able to robustly infer one or more associated actionable
classes for the individuals in order to act automatically on it. We model this
problem as a multi-label classification task where we want to robustly identify
types of the individuals in a semantic graph such as DBpedia, which we use as
an exemplary dataset on the Semantic Web. Our approach first extracts multiple
features for the individuals using random walks and then performs multi-label
classification using fully-connected Neural Networks. Through systematic
exploration and experimentation, we identify the effect of hyper-parameters of
the feature extraction and the fully-connected Neural Network structure on the
classification performance. Our final results show that our method performs
better than state-of-the-art inferencing systems like SDtype and SLCN, from
which we can conclude that random-walk-based feature extraction of individuals
and their multi-label classification using Deep Neural Networks is a promising
alternative to these systems for type classification of individuals on the
Semantic Web. The main contribution of our work is to introduce a novel
approach that allows us to use Deep Neural Networks to identify types of
individuals in a noisy semantic graph by extracting features using random walks
