Temporal Knowledge Graphs (TKGs) are knowledge graphs where every fact is explicitly anchored in time (a timestamp or interval), so you can represent and reason about how entities and relations change, not just how they are related “in general.”[1][2]

Core idea from the Medium piece

The article’s key move is simple but powerful: take a normal KG (entities + relations) and attach temporal context to edges, so facts become quadruples like ((subject, relation, object, time)).[3][1] For your purposes, that means any statement in your knowledge base should be made about “what was true when,” not “what is timelessly true.”[4][1]

Concretely, instead of:

• (Model X, supports, Feature Y)

you store:

• (Model X, supports, Feature Y, 2024-08–2025-03)

This lets you later ask: “What did I believe about Feature Y in 2024?” versus “What is current now?”[1][3]

Why TKGs matter for organizing your knowledge

For an evolving research and systems-design practice like yours, TKGs directly support:

• Tracking conceptual drift: how definitions, taxonomies, and framings (e.g., of Indigenous-AI integration, NCP, etc.) have shifted over time.[4][1]
• Versioned system knowledge: which architectures, prompts, or protocols were in use during a given project or experimental phase.[3][1]
• Temporal queries: questions of the form “What did I think/assume before X?” or “How did my position change after Y event?” become natural queries over time-stamped edges.[2][5]

An illustration: you could encode your evolving definition of “Narrative Context Protocol” as a series of time-scoped triples that point to different design docs, diagrams, or code branches active in different periods.[2][1]

Minimal design pattern to apply TKGs to your own notes

To adopt the essence of TKGs without over-engineering:

1. Adopt a quadruple schema in your knowledge store

   • Base unit: ((entity_1, relation, entity_2, time_scope)).[3]
   • time_scope can be a point (“2025-11-01”), an interval (“2024-03–2024-09”), or a qualitative period (“pre-ICEWS-experiments”) as long as you use it consistently.[6][1]

2. Annotate temporal scope at capture time

   • When you add a note, concept map, or link between concepts, ask: “From when to when is this believed or operative?” and store that alongside the relation.[1]
   • Even a coarse tag like “as of 2026-03” gives you future leverage for temporal queries.[2][4]

3. Organize your queries around time-aware questions

   • “What were my assumptions about automated trading before adopting architecture Z in 2025?”
   • “How has my framing of ‘decolonizing data’ changed since 2023?”
     These map to temporal KG question answering, where you filter or order paths by time constraints.[7][3]

You can implement this inside whatever system you already use (Obsidian, a graph DB, or a custom .NET backend) by making sure every edge has a time field and your retrieval layer exposes filters and ordering over that field.[5][1]

Practical application patterns in your context

Here are a few concrete ways to apply TKG thinking to your existing domains:

• Research & literature review

  • Represent papers and concepts as entities, with relations like (Concept A, refines, Concept B, 2022–2024) or (Paper P, criticizes, Approach Q, 2024).[8][2]
  • You can then trace how a debate evolved or which models were state-of-the-art at a given time.

• Systems and protocol design (NCP, state machines, RAG)

  • Encode design decisions as time-scoped facts: (System S, uses, Component C, 2023-09–2024-02).[5][1]
  • When you refactor, you add new facts with later time intervals instead of overwriting, preserving a temporal lineage of your architecture.

• Personal epistemic history

  • Model your own stance shifts: (User, endorses, View V, 2021–2023), (User, rejects, View V, 2024–).[4][1]
  • This becomes a temporal map of your thinking you can query and visualize.

If you tell me your stack, I can sketch a concrete schema

If you share what you’re currently using to represent your notes/graphs (e.g., Neo4j vs. a markdown+YAML layer vs. a custom C# service), I can propose a minimal temporal schema and a couple of query patterns that implement the above TKG ideas without dragging you into full research-grade TKG tooling.[1][2][4]

Sources [1] Temporal Knowledge Graphs https://www.emergentmind.com/topics/temporal-knowledge-graphs [2] A Survey on Temporal Knowledge Graph: Representation Learning and Applications https://arxiv.org/abs/2403.04782v1 [3] A temporal knowledge graph reasoning model based on recurrent ... https://pmc.ncbi.nlm.nih.gov/articles/PMC11784877/ [4] Temporal Knowledge Graphs - Emergent Mind https://www.emergentmind.com/topics/temporal-knowledge-graphs-tkgs [5] A temporal knowledge graph reasoning model based on recurrent encoding and contrastive learning https://peerj.com/articles/cs-2595.pdf [6] A Temporal Knowledge Graph Generation Dataset Supervised Distantly by Large Language Models https://www.nature.com/articles/s41597-025-05062-0 [7] Self-Improvement Programming for Temporal Knowledge ... https://aclanthology.org/2024.lrec-main.1270.pdf [8] A Survey on Temporal Knowledge Graph Completion https://axi.lims.ac.uk/paper/2308.02457 [9] Temporal Knowledge Graph Forecasting Without ... - LinkedIn https://www.linkedin.com/posts/research-graph_temporal-knowledge-graph-forecasting-without-activity-7186179638337961984-vsKb [10] Pluriverse – Lens 1 (personal) - Weblearning https://weblearning.co.za/blog/pluriverse-lens-1-personal/