SSTorytime (A Unified Graph Process For Mapping Knowledge)

Mark Burgess
Nov 8, 2025
4 min read

Updated: Nov 14, 2025

Imagine a tool that would help you to know your own thinking, to capture it, visualize it, and make it searchable for those days when your scatterbrain isn’t working on all cylinders. This is the thinking behind SSTorytime. We might not call such a tool Artificial Intelligence; rather, we might call it a "cyborg enhancement". Still, the results would be useful for training and teaching of human or machine intelligence alike. Such a toolset is the goal of the open source SSTorytime Knowledge project. The issue of what kind of graph you should make is secondary to the issue of understanding what knowledge means, and how we will use it. Neither topic maps nor rdf communities got this right in the past.

Get going with the documentation or keep scrolling for some background and highlights...

SSTorytime is an independent Knowledge Graph, based on Semantic Spacetime. It is not an Topic Map or RDF-based project. It aims to be both easier to use and more powerful than RDF.

Graphs are the language of spacetime process

Graphs are popular once again, but the technologies for dealing with them are clunky and designed by technologists rather than scientists. This project makes working with graphs simple.

Graphs may be used:

As visualization of processes.
As a map of space and time.
As a map of a process, like Gant charts and path integrals.
As computational device (a multi-matrix algebra representation).
- e.g. social networks with centralities and flow patterns, link weight as contact frequencies...
As a distributed index over semantic relationships.
And more ...

If you want to know the deep background behind the Semantic Spacetime concept and its approach, you can read the book shown to the right.

N.B. This book is conceptual background, not a tutorial or HOW-TO manual.

Deep Dive into this Semantic Spacetime Project (SST)

Keywords, tags: Open Source Smart Graph Database API for Postgres, Go(lang) API, Explainability of Knowledge Representation

This project aims to turn intentionally created data (like written notes or snippets cut and pasted into a file) into linked and searchable knowledge maps, tracing the stories that we call reasoning, and solving puzzles by connecting the dots between bits of information you curate.

For instance...

You might want to look up something ad hoc:

You might want to be prompted for reminders:

You might be surveying the breadth of your knowledge:

Or just curious about something:

The concept

Knowledge maps are graph (network) structures that link together events, things, and ideas into a web of relationships. They have enjoyed renewed interest in recent years, because of "AI" -- but, since the 1990s, people have largely been doing them wrong--trying to model things and ideas instead of processes.

Graphs are great ways to find out where processes start and stop, who is most important in their execution, the provenance of transacted things or ideas, and their rate of spreading, etc etc. The pathways through such a web form journeys, histories, or stories, planning itineraries or processes, depending on your point of view. We can interpret graphs in many ways. Your imagination is the limit,

Stories are one of the most important forms of information, whether they describe happenings, calculations, tales of provenance, system audits... Stories underpin everything that happens.

Getting data into story form isn't as easy as it sounds, so we start by introducing a simple language "N4L" to make data entry as painless as possible. Then we add tools for browsing, visializing, analysing the resulting graph, solving for paths, and divining storylines through the data. The aim is to support human learning, and to assist human perception--though the results may be used together with "AI" in the future. Finally, there will be an API for programmers to incorporate these methods into their own explorations, either in Go or in Python. As a sort of "better, faster Python", Go is recommended for power scripting.

Note-taking may be an intuitive but semi-formal approach to getting facts for reasoning, for knowledge capture, querying, and dissemination of individual thinking easy, for humans and general use. (AI can only capture knowledge from humans, so even if we want to use AI, we'd better get the knowledge representations right.) Whether we are analysing forensic evidence, looking for criminal behaviour, learning a foreign language, or taking notes in school for an exam.