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Chapter 14
Representing Implicit Knowledge via Hypergraphs

14.1 Introduction

Explicit knowledge is easy to write about and talk about; implicit knowledge is equally impor-
tant, but tends to get less attention in discussions of AI and psychology, simply because we don’t
have as good a vocabulary for describing it, nor as good a collection of methods for measuring
it. One way to deal with this problem is to describe implicit knowledge using language and
methods typically reserved for explicit knowledge. This might seem intrinsically non-workable,
but we argue that it actually makes a lot of sense. The same sort of networks that a system like
CogPrime uses to represent knowledge explicitly, can also be used to represent the emergent
knowledge that implicitly exists in an intelligent system’s complex structures and dynamics.

We’ve noted that CogPrime uses an explicit representation of knowledge in terms of weighted
labeled hypergraphs; and also uses other more neural net like mechanisms (e.g. the economic
attention allocation network subsystem) to represent knowledge globally and implicitly. Cog-
Prime combines these two sorts of representation according to the principle we have called
glocality. In this chapter we pursue glocality a bit further – describing a means by which even
implicitly represented knowledge can be modeled using weighted labeled hypergraphs similar to
the ones used explicitly in CogPrime. This is conceptually important, in terms of making clear
the fundamental similarities and differences between implicit and explicit knowledge represen-
tation; and it is also pragmatically meaningful due to its relevance to the CogPrime methods
described in Chapter 42 of Part 2 that transform implicit into explicit knowledge.

To avoid confusion with CogPrime’s explicit knowledge representation, we will refer to the
hypergraphs in this chapter as composed of Vertices and Edges rather than Nodes and Links. In
prior publications we have referred to "derived" or "emergent" hypergraphs of the sort described
here using the acronym SMEPH, which stands for Self-Modifying, Evolving Probabilistic Hy-
pergraphs.

14.2 Key Vertex and Edge Types

We begin by introducing a particular collection of Vertex and Edge types, to be used in modeling
the internal structures of intelligent systems.
The key SMEPH Vertex types are

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