Spacetimes with Semantics (III) - The Structure of Functional Knowledge Representation and Artificial Reasoning

TL;DR

This paper explores how semantic spacetime concepts can underpin knowledge representation and reasoning, suggesting that key aspects of intelligence emerge from fundamental spacetime structures within a unified framework.

Contribution

It introduces a novel interpretation of knowledge systems using promise theory and semantic spacetime, highlighting principles for effective knowledge representation and the emergence of intelligence.

Findings

Identification of four types of associations: aggregation, causation, cooperation, similarity

Principles for effective knowledge representation including scale separation and identity discrimination

Unified framework connecting information models, machine learning, and semantic networking

Abstract

Using the previously developed concepts of semantic spacetime, I explore the interpretation of knowledge representations, and their structure, as a semantic system, within the framework of promise theory. By assigning interpretations to phenomena, from observers to observed, we may approach a simple description of knowledge-based functional systems, with direct practical utility. The focus is especially on the interpretation of concepts, associative knowledge, and context awareness. The inference seems to be that most if not all of these concepts emerge from purely semantic spacetime properties, which opens the possibility for a more generalized understanding of what constitutes a learning, or even `intelligent' system. Some key principles emerge for effective knowledge representation: 1) separation of spacetime scales, 2) the recurrence of four irreducible types of association, by which intent propagates: aggregation, causation, cooperation, and similarity, 3) the need for discrimination of identities (discrete), which is assisted by distinguishing timeline simultaneity from sequential events, and 4) the ability to learn (memory). It is at least plausible that emergent knowledge abstraction capabilities have their origin in basic spacetime structures. These notes present a unified view of mostly well-known results; they allow us to see information models, knowledge representations, machine learning, and semantic networking (transport and information base) in a common framework. The notion of `smart spaces' thus encompasses artificial systems as well as living systems, across many different scales, e.g. smart cities and organizations.