  The recent developments and growing interest in neural-symbolic models has
shown that hybrid approaches can offer richer models for Artificial
Intelligence. The integration of effective relational learning and reasoning
methods is one of the key challenges in this direction, as neural learning and
symbolic reasoning offer complementary characteristics that can benefit the
development of AI systems. Relational labelling or link prediction on knowledge
graphs has become one of the main problems in deep learning-based natural
language processing research. Moreover, other fields which make use of
neural-symbolic techniques may also benefit from such research endeavours.
There have been several efforts towards the identification of missing facts
from existing ones in knowledge graphs. Two lines of research try and predict
knowledge relations between two entities by considering all known facts
connecting them or several paths of facts connecting them. We propose a
neural-symbolic graph neural network which applies learning over all the paths
by feeding the model with the embedding of the minimal subset of the knowledge
graph containing such paths. By learning to produce representations for
entities and facts corresponding to word embeddings, we show how the model can
be trained end-to-end to decode these representations and infer relations
between entities in a multitask approach. Our contribution is two-fold: a
neural-symbolic methodology leverages the resolution of relational inference in
large graphs, and we also demonstrate that such neural-symbolic model is shown
more effective than path-based approaches
