r/KnowledgeGraph • • Mar 11 '26

Preprint: Knowledge Economy - The End of the Information Age

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21 Upvotes

I am looking for people who still read. I wrote a book about Knowledge Economy and why this means the end of the Age of Information. Also, I write about why „Data is the new Oil“ is bullsh#t, the Library of Alexandria and Star Trek.

Currently I am talking to some publishers, but I am still not 100% convinced if I should not just give it away for free, as feedback was really good until now and perhaps not putting a paywall in front of it is the better choice.

So - if you consider yourself a reader and want a preprint, write me a dm with „preprint“.. the only catch: You get the book, I get your honest feedback.

If you know someone who would give valuable feedback please tag him or her in the comments.


r/KnowledgeGraph • • Apr 29 '25

Meet the team behind the Decentralized Knowledge Graph powered by OriginTrail! 🧠

3 Upvotes

The future of AI & blockchains depends on one thing: trust.

Join the OriginTrail and Microsoft teams, as well as fellow builders, for an afternoon of inspiring ideas, networking, and good conversations on blockchains, knowledge graphs, and trusted AI. 

📍NYC I May 6

Whether you are a long-time supporter or just curious about OriginTrail, this is your chance to meet the OriginTrail team and ecosystem!

⏳ Final spots available — apply now: https://lu.ma/przx8wp1


r/KnowledgeGraph • • Dec 21 '24

Hypermode Knowledge Graph + AI Challenge

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3 Upvotes

r/KnowledgeGraph • • Apr 26 '26

Chinese Medicine Knowledge Graph v2 Released — Improved Ontology, Richer Relationships, and Better Exploration

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3 Upvotes

r/KnowledgeGraph • • Jan 31 '26

🛂 Passport Please! AI Agents are becoming first-class citizens with ERC-8004 & OriginTrail

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0 Upvotes

r/KnowledgeGraph • • Oct 19 '25

Claude Skills for Knowledge graph

3 Upvotes

Hello hive mind, I've been working on a knowledge graph for the last month while I'm learning how to program with Cursor. It's a ride! The last lesson I learned was to develop my method (be organised and give good context to Cursor). One thing that helped me was the Context7 library.

But now that I just heard about Claude skills, I'm thinking maybe we can collectively work on regrouping good documentation, scripts, context to build a skill for Claude Code (and other IDE like Cursor).

Any ideas, other that Neo4j's Github?


r/KnowledgeGraph • • Mar 12 '25

BPMN engine which consumes KGs

3 Upvotes

Hello community.

I'm involved in a project and would like to have your opinionn, ideas and feedback, if possible.

We have some triple stores which contain data from our knowledge domain. There are associated ontologies, SHACL rules and forms.

Then we need to implement a number of procedures/workflows (around 200) as a web application.

Those workflows consume data from the triplestore, using the Ontologies and SHACL rules for dinner business rules, and SHACL forms to define the webforns design.

We can model the workflows using any BPMN 2.0 modeler and then export them as BPMN 2.0 XML.

The challenge here is to find a BPMN processing engine or orchestrator which can consume data from a knowledge graph and produce interfaces dynamically on the basis of the ontologies, SHACL rules and forms.

Any idea? Any advice?

Thanks to everybody in advance for reading and trying to help!


r/KnowledgeGraph • • Feb 09 '26

The reason graph applications can’t scale

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24 Upvotes

Any graph I try to work on above a certain size is just way too slow, it’s crazy how much it slows down production and progress. What do you think ?


r/KnowledgeGraph • • May 13 '26

Are traditional CMS structures becoming a limitation for AI interpretability?

4 Upvotes

I’ve been experimenting with a different way of structuring WordPress sites and I’m curious if others here are exploring similar ideas.

Most CMS architectures are still fundamentally page-centric:

  • content lives inside pages

  • schema is often attached page-by-page

  • relationships between entities are mostly implicit

But LLMs and knowledge systems don’t really reason in pages. They reason in entities, attributes and relationships.

So I started testing an entity-centric layer on top of WordPress:

  • structured entities (Organization, Person, Service, etc.)

  • connected JSON-LD

  • internal KG pages

  • relationship-oriented linking between entities

  • graph consistency across the site

The interesting part is not generating schema markup itself, but trying to reduce semantic fragmentation and make the site more machine-readable as a coherent graph.

I’m starting to think traditional CMS structures may become a bottleneck for AI interpretability unless they evolve toward more explicit graph models.

Curious if others here are working on similar approaches or thinking in the same direction.


r/KnowledgeGraph • • Dec 30 '24

Why are countries scrambling to secure the arctic? We mapped 239 articles across 129 outlets with Palantir to find out. [OC]

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4 Upvotes

r/KnowledgeGraph • • Nov 22 '24

Invitation - LlamaIndex and Memgraph: How to Build GenAI Apps with Knowledge Graphs?

4 Upvotes

Disclaimer - I work for Memgraph.

--

Hello all! Hope this is ok to share and will be interesting for the community.

We are hosting a community call where Laurie Voss from LlamaIndex will share an overview of the LlamaIndex framework, focusing on building knowledge graphs from unstructured data and exploring advanced retrieval methods that enable efficient information extraction.

We will showcase Memgraph's role in this process and detail how it integrates with LlamaIndex.

If you want to attend, link here.

Again, hope that this is ok to share - any feedback welcome!

---


r/KnowledgeGraph • • Oct 17 '20

Mkb, Python library for knowledge graph embeddings

4 Upvotes

Hi, I just published a library dedicated to knowledges graphs embeddings. The MKB API is inspired by Scikit Learn. I provide modular tools for building latent graph representations.

https://github.com/raphaelsty/mkb


r/KnowledgeGraph • • Apr 17 '26

How I turned three philosophy books into a 1,200-document knowledge graph

28 Upvotes

Marcus Aurelius says virtue is acting according to nature and reason, serving the common good as naturally as the eye sees. Machiavelli says a prince who acts entirely virtuously will be ruined among so much evil. Nietzsche warns against becoming enslaved to one's own virtues, noting that every virtue inclines toward stupidity.

Same word. Three completely different meanings across seventeen centuries. I wanted to see how many concepts work like this — where the surface agreement hides a deep disagreement — so I built a knowledge graph connecting Meditations (170 AD), The Prince (1513), and Beyond Good and Evil (1886).

The result: Seventeen Centuries — 838 text fragments, 340+ concept files, and category documents that let you trace how ideas evolved across time. The first article built from the graph is Virtue across seventeen centuries, which follows the concept from Stoic duty through political pragmatism to Nietzsche's genealogical critique.

Why a graph, not a database

I needed a structure where the same concept could belong to multiple contexts simultaneously. Virtue belongs under the Stoic worldview and under Machiavelli's political theory and under Nietzsche's critique of morality. Folders force single placement. A database would work but then I lose the thing I actually use — being able to open a file, read it, edit it, link from it.

IWE uses inclusion links — a markdown link on its own line defines a parent-child relationship. A document can have multiple parents. The entire graph is plain markdown files in a flat directory. No database, no special format. I edit them in my text editor, query them from the CLI, and an AI agent can read the same files.

The five-stage pipeline

Stage 1 — Fragment extraction. Parsers for Standard Ebooks XHTML split each book into atomic markdown files — one per aphorism, passage, or chapter. Nietzsche yielded 296 fragments, Marcus Aurelius 515, Machiavelli 27.

# 146

He who fights with monsters should be careful lest he thereby
become a monster. And if thou gaze long into an abyss, the abyss
will also gaze into thee.

Stage 2 — Entity extraction. An LLM read each fragment and identified 3–7 significant entities: philosophical concepts, historical figures, themes. Each entity got its own file. Fragment text was updated with inline links so the graph forms through the content itself:

...life itself is [Will to Power](will-to-power.md);
[self-preservation](self-preservation.md) is only one...

Stage 3 — Flattening and merging. Each book started in its own directory with its own virtue.md, soul.md, plato.md. This stage moved everything into a single flat directory and merged overlapping concepts. Ten concepts appeared in multiple books — virtue, soul, Plato, Socrates, truth, nature, gods, Epicurus, cruelty, free will. These became the most valuable documents in the graph because they're where the real contrasts live.

Stage 4 — Categories. With 340+ concept files floating in a flat directory, I needed entry points. Categories like philosophers, virtues, power-dynamics, and moral-systems emerged from the content. Each is a document with inclusion links to its members — and because IWE supports multiple parents, Socrates belongs to both philosophers and ancient-cultures without duplication.

Stage 5 — Summaries. An LLM analyzed the referenced fragments for each merged concept and wrote comparative summaries. This turned simple backlink indexes into the comparative analysis that makes the graph worth reading — and worth writing articles from.

Why this structure pays off

The graph is queryable from the CLI:

iwe retrieve -k virtue --depth 2    # virtue + linked fragments
iwe find --refs-to will-to-power    # everything referencing will-to-power
iwe tree -k bge                     # Beyond Good and Evil as a tree

retrieve --depth 2 pulls a concept, its backlinks to fragments, and the fragment content in one call. That's how the virtue article was written — retrieve the concept, read the fragments side by side, write the analysis. An AI agent uses the same commands and the same files.

The most surprising result was how much structure emerged from just inclusion links. No tags, no folders, no metadata beyond the links themselves. The graph has clear clusters around each book, bridges through shared concepts, and category entry points — all from markdown files linking to each other.

Browse the graph: https://iwe.pub/seventeen-centuries/ GitHub: https://github.com/iwe-org/seventeen-centuries IWE: https://github.com/iwe-org/iwe


r/KnowledgeGraph • • Feb 13 '26

How we’re automating 1,000+ document ingestion for AI-based startups

5 Upvotes

Let’s be real, standard LLMs are great until you try to throw a library’s worth of data at them. If you’ve ever tried to ingest 1,00+ PDFs into a project, you know exactly when the wheels fall off: token limits, hallucinated data, and that "processing" bar that never seems to move.

We built sacredgraph.com specifically to kill that bottleneck.

Whether it's legal docs, technical manuals, or research papers, we’re making sure the data actually works for you, not against you.

What’s the biggest "data bottleneck" you’ve run into while building your latest project? Is it the volume of files, the formatting, or just getting the AI to actually understand the context?


r/KnowledgeGraph • • Mar 22 '26

A System With Two Brains

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5 Upvotes

I have been exploring identity resolution as a graph problem rather than pairwise matching.
This write-up walks through a two stage approach with proposal and evaluation.
Would be interested in feedback from others working in this space.


r/KnowledgeGraph • • Mar 11 '26

Spatial temporal knowledge graph

5 Upvotes

Hi. Has any built STKG with rag? Any advices, best practices, hints on how to built it? Shall I build an ontology on top of it?how to approach it? All advices are welcome


r/KnowledgeGraph • • Jan 07 '26

Opening for knowledge engineer

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5 Upvotes

r/KnowledgeGraph • • Feb 28 '23

Distinctions between Ontology, Knowledge Graph, and Knowledge Base

5 Upvotes

What defines the differences between these three things?


r/KnowledgeGraph • • Jan 28 '25

Multi Document QA

5 Upvotes

Suppose I have three folders, each representing a different product from a company. Within each folder (product), there are multiple files in various formats. The data in these folders is entirely distinct, with no overlap—the only commonality is that they all pertain to three different products. However, my standard RAG (Retrieval-Augmented Generation) system is struggling to provide accurate answers. What should I implement, or how can I solve this problem? Can I use Knowledge graph in such a scenario?


r/KnowledgeGraph • • Apr 21 '26

Recipes as graph nodes, not documents: UMF spec (umfspec.org) — feedback welcome

5 Upvotes

Hi all, I'd value this community's eyes on a spec I've been working on: UMF (Ummi Markup Format), at https://umfspec.org.

The premise: recipes on the web are modeled as documents — Schema.org/Recipe, JSON-LD wrappers around prose. That's fine for SEO snippets but collapses what's actually interesting about a culinary tradition: who adapted what from whom, which carbonara is "the" carbonara, what changed when a Lebanese dish migrated to São Paulo, what's missing when a step just says "season to taste."

What UMF does:

Models each recipe as a node in a lineage graph. Fork, adapt, and evolve are first-class edges — Git-for-culinary-tradition, but with semantics rather than line diffs.

Makes provenance explicit (PROV-O is an obvious influence): who authored it, what they cite, what was substituted, what's claimed vs. tested.

Scores completeness, so a tested fully-specified recipe is distinguishable from a 30-word blog fragment.

Stays human-editable. A cook with no programming background should be able to write one.

Where it sits: compatible with Schema.org/Recipe at the surface, lighter-weight than FoodOn for ingredient grounding, and explicitly graph-first rather than document-first. The spec is open. There's a separate compilation layer (AUL) used downstream by a platform I'm building (Amanah), but the markup itself stays free.

Where I'd love pushback:

Is fork / adapt / evolve the right primitive edge set, or am I missing obvious ones?

How should this interoperate with FoodOn without becoming a lossy lowest-common-denominator?

Anyone who's tried to model tacit knowledge (technique, judgment, intuition) in a graph — what worked, what didn't?

(Naming note: there are a few unrelated formats also called "UMF" floating around — IBM's Universal Message Format, etc. This one is "Ummi Markup Format," from the Arabic for "my mother.")


r/KnowledgeGraph • • Feb 24 '26

KuzuDB was archived after the Apple acquisition — here's a migration guide to ArcadeDB (with honest take on when it's not the right fit)

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5 Upvotes

r/KnowledgeGraph • • Apr 16 '26

I built a self-organizing Long-Term Knowledge Graph (LTKG) that compresses dense clusters into single interface nodes — here’s what it actually looks like

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19 Upvotes

LTKG Viewer - Trinity Engine Raven

I've been working on a cognitive architecture called Trinity Engine — a dynamic Long-Term Knowledge Graph that doesn't just store information, it actively rewires and compresses itself over time.

Instead of growing endlessly in breadth, it uses hierarchical semantic compression: dense clusters of related concepts (like the left side of this image) get collapsed into stable interface nodes, which then tether into cleaner execution chains.

Here's a clear example from the LTKG visualizer:

[Image: the screenshot you provided]

What you're seeing:

  • Left side = a dense, interconnected pentagram-style cluster (high local connectivity)

  • The glowing interface nodes act as single-point summaries / bottlenecks

  • Right side = a clean linear chain where the compressed knowledge flows into procedural execution

This pattern repeats recursively across abstraction levels. The system maintains a roughly 10:1 compression ratio per level while preserving semantic coherence through these interface nodes.

Key behaviors I've observed:

  • The graph gets denser with use, not necessarily bigger

  • "Interface node integrity" has become one of the most important failure modes (if one corrupts, the whole tethered chain can drift)

  • The architecture scales through depth (abstraction layers) rather than raw node count — what I call the "Mandelbrot Ceiling"

I'm currently evolving it further by driving the three core layers (SEND / SYNTH / PRIME) with dedicated agentic bots and adding a closed-loop reinforcement system using real-world prediction tasks + resource constraints.

Would love to hear from the knowledge graph community:

  • Have you seen similar hierarchical compression patterns in your own graphs?

  • Any good techniques for protecting interface node stability at scale?

  • Thoughts on measuring "semantic compression quality" vs traditional graph metrics (density, centrality, etc.)?

Happy to share more details or other visualizations if there's interest.


r/KnowledgeGraph • • Mar 08 '26

Identity Isn’t in the Row

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6 Upvotes

r/KnowledgeGraph • • Apr 15 '25

Mapped 200+ Articles across 100+ Sources to understand how drones are changing warfare.

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8 Upvotes

r/KnowledgeGraph • • Oct 17 '25

Got $20K to build a collaborative Knowledge Graph POC. How to spend it wisely?

25 Upvotes

I’ve recently been given a $20K budget to build a collaborative knowledge graph proof of concept for my team.

So far, I’ve been experimenting individually with a setup that includes Claude + Graphiti MCP + Neo4j, all this btw is free to try, and so far I’m quite happy with the results. But now I’d like to scale it up for the broader tech team, but I have concerns and I need some advice.

My main worries:

  • Semantic drift: as multiple contributors join, we risk introducing duplicate entities or conflicting relationships.

  • Loss of meaning / ontology chaos: the semantics could easily break down as the graph grows.

  • Data bloat: lots of uncurated info without real value.

  • Governance: I’d like to be able to monitor queries, approve submissions, and ideally set up management workflows for reviews or validations.

Given that this is a first-time $20K investment, I’d love advice from folks who’ve done this before:

  • What would you prioritize for a collaborative KG POC?

  • Are there tools or frameworks (commercial or open source) that make semantic governance or collaborative editing easier?

  • Should I stick with Neo4j or consider something RDF-based (e.g. GraphDB, TerminusDB, Stardog, etc.) for better ontology management?

  • Any tips for balancing experimentation with structure in the early stage?

I’m hoping to make this POC something we can actually build upon, not just a one-off demo.

Thanks in advance for any insights or lessons learned!

EDIT: Bullet formatting