  Reasoning is a fundamental problem for computers and deeply studied in
Artificial Intelligence. In this paper, we specifically focus on answering
multi-hop logical queries on Knowledge Graphs (KGs). This is a complicated task
because, in real-world scenarios, the graphs tend to be large and incomplete.
Most previous works have been unable to create models that accept full
First-Order Logical (FOL) queries, which include negative queries, and have
only been able to process a limited set of query structures. Additionally, most
methods present logic operators that can only perform the logical operation
they are made for. We introduce a set of models that use Neural Networks to
create one-point vector embeddings to answer the queries. The versatility of
neural networks allows the framework to handle FOL queries with Conjunction
($\wedge$), Disjunction ($\vee$) and Negation ($\neg$) operators. We
demonstrate experimentally the performance of our model through extensive
experimentation on well-known benchmarking datasets. Besides having more
versatile operators, the models achieve a 10\% relative increase over the best
performing state of the art and more than 30\% over the original method based
on single-point vector embeddings.
