  Graph data structures are widely used to store relational information between
several entities. With data being generated worldwide on a large scale, we see
a significant growth in the generation of knowledge graphs. Thing in the future
is Orange's take on a knowledge graph in the domain of the Web Of Things (WoT),
where the main objective of the platform is to provide a digital representation
of the physical world and enable cross-domain applications to be built upon
this massive and highly connected graph of things. In this context, as the
knowledge graph grows in size, it is prone to have noisy and messy data. In
this paper, we explore state-of-the-art knowledge graph embedding (KGE) methods
to learn numerical representations of the graph entities and, subsequently,
explore downstream tasks like link prediction, node classification, and triple
classification. We also investigate Graph neural networks (GNN) alongside KGEs
and compare their performance on the same downstream tasks. Our evaluation
highlights the encouraging performance of both KGE and GNN-based methods on
node classification, and the superiority of GNN approaches in the link
prediction task. Overall, we show that state-of-the-art approaches are relevant
in a WoT context, and this preliminary work provides insights to implement and
evaluate them in this context.
