Knowledge Capture

Exiting university there was something (other than loans) that was bothering me. I began to notice a trend, a frustration really, almost imperceptible, like something at the corner of your periphery, lurking in the shadows. This frustration from various aspects of my schooling and personal research coalesced, I was very unhappy with the way information, or more abstractly, knowledge was stored and organised.

Note: This is a live document and will be expanded upon as this project progresses

Titles begin to establish context

From cave paintings to blueray disks, and electrons held captive in silicon, despite the medium we have always codified knowledge using natural language. Except natural language requires context to be understood, which means the meaning can be lost with the context. Is the context necessary? Does it need to be within the context of human experience? These questions and similar ones span around me as I delved deeper into information theory. I wanted a reader and language agnostic way of storing knowledge so that it could be made available to computer systems. Computers have no context, everything is just numbers, and they operate on them with little regard for their meaning. So how do you store knowledge in a manner that allows an algorithm to extrapolate information?

I have a hypothesis: all knowledge can be described or implied within the context of a process. I wish I could remember the exact line of thinking that led me to this hypothesis, but alas, the context was lost. What I do remember is that I was looking for a uniform way to represent knowledge, and rather than try to remove context, find a universal context. Haphazardly I settled on the idea that a process would be the best way forward. The idea is predicated on the concept of causality and that everything that exists is the result of a deterministic process.