As Knowledge Graphs are increasingly recognized as an important approach to solving problems related to semantic understanding beyond various question and answering systems, various solutions focusing on Knowledge Graphs have been proposed in the background of relationship defined everything of semantics (RDXS) towards solving essence oriented computation and reasoning activities. These approaches cover Knowledge Graph creation, understanding, searching, reasoning, modification and especially and most recently embedding technologies in the form of various Graph integration Machine Learning extensions. A foreseeable AI landscape with explainable and interactive human interactions is becoming feasible based on Knowledge Graphs premises. The boundaries of the capability of Knowledge Graph usages are constantly expanding, but there are also open questions as to what issues can be solved by Knowledge Graphs alone, what forms of content are optimal to be mapped and processed efficiently and effectively in the forms of extensions of Knowledge Graphs. Hierarchical architectures which project Data, Information, Knowledge and Wisdom (DIKW) seem to be well paired with the organizing capability of Knowledge Graph technologies in terms of the 5W (What, Where, When, How and Why). Although not yet formally settled as a uniform concept itself, Knowledge Graphs have been actually or implicitly functioning as Data Graph, Information Graph, Knowledge Graph and Wisdom Graph towards solving multiple modals, multiple dimensional multiple scale, multiple scales or even mesocale content/resources according to the DIKW hierarchy. Recently we have also seen the emergence of various applications and models of Knowledge Graph as a Service (KGaaS) as a gradual acceleration towards an era of strong AI solutions in cross-modal domains and mesoscale categories in contrast to the currently prevailing weak AI which aims mainly at single modal and inner scales content.