  Knowledge Graphs (KGs) have become increasingly common for representing
large-scale linked data. However, their immense size has required graph
learning systems to assist humans in analysis, interpretation, and pattern
detection. While there have been promising results for researcher- and
clinician- empowerment through a variety of KG learning systems, we identify
four key deficiencies in state-of-the-art graph learning that simultaneously
limit KG learning performance and diminish the ability of humans to interface
optimally with these learning systems. These deficiencies are: 1) lack of
expert knowledge integration, 2) instability to node degree extremity in the
KG, 3) lack of consideration for uncertainty and relevance while learning, and
4) lack of explainability. Furthermore, we characterise state-of-the-art
attempts to solve each of these problems and note that each attempt has largely
been isolated from attempts to solve the other problems. Through a
formalisation of these problems and a review of the literature that addresses
them, we adopt the position that not only are deficiencies in these four key
areas holding back human-KG empowerment, but that the divide-and-conquer
approach to solving these problems as individual units rather than a whole is a
significant barrier to the interface between humans and KG learning systems. We
propose that it is only through integrated, holistic solutions to the
limitations of KG learning systems that human and KG learning co-empowerment
will be efficiently affected. We finally present our "Veni, Vidi, Vici"
framework that sets a roadmap for effectively and efficiently shifting to a
holistic co-empowerment model in both the KG learning and the broader machine
learning domain.
