Knowledge Representation using Interlinked Construct Nodes

What if you could represent knowledge and human cognition in a uniform and consistent manner, across all forms and scopes of knowledge, so that a humanoid robot could understand, work, and behave in the world as we do? The answer to this question is what motivated me to invent a novel method of knowledge representation using Interlinked Construct Nodes (ICN).

ICN Graphs represent knowledge about words, phrases, sentences, images, sounds, videos, symbols, equations, formulas, organisms, objects, processes, behaviors, events, and concepts. In its graphical embodiment, ICN's paired linking categories make it easy for both humans and machines to understand the knowledge represented, no matter how detailed or complex. ICN Graphs are self-documenting, so there's always an explanation of how the computer reached a conclusion or decision.

The ICN Graph Editor is almost ready for alpha testing. I'm finishing up the Photoshop-style "undo" and "redo" functionality and fixing the jump linking feature. Jump linking allows the user to link one ICN to another ICN by "jumping" to the other ICN on the same or different graph versus dragging a line from one ICN to another ICN.

There's still a lot of work to do, but I'm progressing towards my goal.

U.S. Patent 11334541 B1, "Knowledge Representation using Interlinked Construct Nodes"