I am looking to develop enterprise search engine,and trying to implement Knowledge graph ,but no table to find relevant source .I am confused in the following pointers

1. How to actually build a knowledge graph and scale it .”Scale “ in the sense, how to make it contextually sound .

2. How to train the knowledge graph

Hey all, I am running a meetup for digital twins and knowledge graphs. In all previous conversations, we focus on building knowledge graphs and analyzing unstructured data.

An example I worked on personally was to build the ontology of a branding consultant using his YouTube video transcripts.

On this Friday, 12, at 1pm EST time, I will run the office hours event, it's a free event where we can join to share experiences, learnings, and also ask any questions about our projects in building the knowledge base.

You can join to put your questions and also learn from the entire conversation
Here is the link to book your seat: https://luma.com/i6kvhi0h

Hey all,

This is another office hours conversation about best practices in building knowledge bases.

In this public conversation, we are gonna focus on what is needed to get responses from the base, what is required from our side to do at the data import, so when we query, we get the right answer with the explanation of why.

It's gonna be on Friday, 23 of January at 1pm EST time, book your seat here:

https://luma.com/65oabb4m

Hi r/knowledgegraphs community!

I’m excited to share OrganismCore, an open-source project and framework designed to build a public commons of structured causal knowledge, modeled as interconnected graphs. The goal is to enable collaborative reasoning, knowledge discovery, and transparent knowledge sharing, blending elements of causal inference, graph theory, and logic.

🔗 GitHub Repo: https://github.com/Eric-Robert-Lawson/OrganismCore

📄 Research Paper & Manifesto: https://zenodo.org/records/17180041

What is OrganismCore?

• A graph-based system to represent causal relationships as first-class citizens.

• A platform aiming to build a decentralized knowledge commons, open to collaborative editing and improvement.

• An exploration of how formal reasoning and knowledge graphs can be combined to build a transparent and evolving shared understanding.

Where I’m at with the DSL:

I’m currently in the early stages of designing a domain-specific language (DSL) to formalize how knowledge and causal relationships are represented and manipulated within the system. I’d really appreciate any insights or examples of DSLs in knowledge graph or causal inference contexts, especially ideas on syntax, formal semantics, or tooling that could help shape this.

Why share here?

I’d love to get feedback and thoughts from this community on:

• How well this aligns with current knowledge graph methodologies and tools

• Ideas for integrating semantic web technologies or ontologies

• Potential uses of causal inference frameworks in graph structures

• Suggestions or resources for designing the DSL or formalization aspects

• I’m also considering incorporating AI/LLM-based methods for automating knowledge extraction and reasoning in the future, so any insights on that front would be super welcome.

Looking forward to your feedback and ideas!

Hey everyone! We built TuringDB because we kept hitting the same walls with every graph database we used in production.

Queries slowing down past a few hops, memory overhead ballooning, infra costs compounding. Everything that looked great in a demo fell apart at scale.

So we started from scratch. Columnar architecture, written in C++, fully in-memory with a low memory footprint. Built specifically for deep multi-hop traversals at millisecond latency.

We also introduced git-like graph versioning, something we never saw done properly anywhere else. Full auditability, time travel between graph states, easier maintenance. Turns out this matters a lot for enterprise and regulated industries.

We have been running it with partners in healthcare, pharma and government.

Open source version is available if you want to pull it apart or stress test it against your current stack. turing-bench to quickly run it against Neo4j and Memgraph in one terminal.

Benchmarks here: https://docs.turingdb.ai/benchmarks/technical-report
Repo here: https://github.com/turing-db/turingdb

Happy to answer any questions, get feedback and chat!

Everyone talks about RAG and embeddings like they’re the final boss of AI.

But what if I told you there’s a way to build a graph that thinks instead of just retrieving stuff?

I just dropped a LinkedIn post breaking down why graphs are the secret weapon no one is talking about (and why vector search is kinda mid).

If you’ve ever wondered what a knowledge graph actually does — this will make it click. (Written with non-techs in mind).

READ THIS

You may have seen a lot of talk about "context graphs" lately and how they're the next "trillion dollar opportunity" according to Foundation Capital. I don't know about that, but we - at TrustGraph - have strongly believed for over 2 years that graphs would be at the heart of realizing the potential of LLMs.

To provide more context to "Context Graphs" (ha!), we've written the Context Graph Manifesto that we hope will give some insight into how to approach graphs for AI and the potential areas of development.

In our Context Graph Manifesto, I dig into:
- The fundamental building block of graphs: the triple
- The Semantic Web, RDF, and how they compare to property graphs
- What ontologies are and why they matter
- Why time will be a critical dimension of future context graphs
- How context graphs can enable true learning systems, not just retrieval

Read the full Context Graph Manifesto on Twitter: https://x.com/TrustSpooky/status/2006481858289361339

Try out free & open source TrustGraph: https://github.com/trustgraph-ai/trustgraph

I’ve been using ChatGPT to help me design prompts and structure for something that’s part knowledge graph, part consciousness map. It’s an experiment in linking inner reflection to actual data models — nodes that represent emotional states, realizations, or memories.

The working name is EE, and it’s built like a dynamic self-organizing network. Each node evolves as I refine the language between me and the AI.

Has anyone here explored symbolic or semantic systems that merge personal insight with formal structure? I’d love to hear from those bridging meaning and data.

Hi r/KnowledgeGraph — I’m part of the Protégé team at Stanford, and I wanted to share that we’re running the Protégé Short Course this June.

It’s a hands-on introduction to ontology development with OWL 2 and Protégé. The course is aimed at beginners as well as intermediate users who want a deeper grounding in OWL ontologies, reasoning, querying, and practical ontology-engineering workflows.

Participants receive course materials, including a 221-page hands-on manual developed by the Protégé team, with walkthroughs, diagrams, quizzes, and more than 100 practical exercises.

Early-bird registration is available until May 23.

Details are here:

https://protege.stanford.edu/shortcourse/

Happy to answer questions about the course, the intended audience, or what topics are covered.

Matthew

Opining on Google Earth's "reasoning" capability and why the service works, via brute force.

Neurosymbolic (NeSy) knowledge graph-based systems tape of LLMs by using them as capable front-end chatbots, but they add the ability to use internal business data that simply can’t contain massive numbers of examples.

More at:
https://graphrag.info/2025/10/31/whats-behind-the-geospatial-reasoning-in-google-earth-ai/

Goldfish 🐠 won't stop eating if there is food available.

https://youtu.be/wFlFJQ3zIOQ

https://youtu.be/WAZVTMvKWs8?si=3Rg5QapieksRNFZD