  Data encoding is a common and central operation in most data analysis tasks.
The performance of other models, downstream in the computational process,
highly depends on the quality of data encoding. One of the most powerful ways
to encode data is using the neural network AutoEncoder (AE) architecture.
However, the developers of AE are not able to easily influence the produced
embedding space, as it is usually treated as a \textit{black box} technique,
which makes it uncontrollable and not necessarily has desired properties for
downstream tasks. In this paper, we introduce a novel approach for developing
AE models that can integrate external knowledge sources into the learning
process, possibly leading to more accurate results. The proposed
\methodNamefull{} (\methodName{}) model is able to leverage domain-specific
information to make sure the desired distance and neighborhood properties
between samples are preservative in the embedding space. The proposed model is
evaluated on three large-scale datasets from three different scientific fields
and is compared to nine existing encoding models. The results demonstrate that
the \methodName{} model effectively captures the underlying structures and
relationships between the input data and external knowledge, meaning it
generates a more useful representation. This leads to outperforming the rest of
the models in terms of reconstruction accuracy.
