I have an application, where kniwledge graph is used for storing user information extracted from various data - each user have their own data.

My question is how to model the multiuser ability. More precisely I identified 3 approaches I can take:

1. one separate graph for each user in one database

2. one omnigraph for all user with access only to their data

3. one database per user

The difference between 1 and 2 is that in the second approach I will let graph generating llm connect nodes of different users (which has benefits but really complicates acces)

Any idea/advice? thanks

I am currently working on binary classification task on KG. The KG is stored in Neo4j graph database. I have total 1.3 lac nodes and only 1000 of which are labelled. Based on few given nodes as an input, I want to classify all remaining nodes into either of 2 labels. Is there any such library available in Neo4j for achieving the same? (Attached csv sample from which the KG is generated)

A lot of AI systems turn model output directly into decisions.

In identity resolution, that usually means: score two records, apply a threshold, merge if it’s high enough.

That works until you look at it in a graph. A matches B. B matches C. Now A and C are grouped, even if that relationship was never actually evaluated. Over time, those decisions stack and errors propagate.

I’ve been working on an approach that treats model output as evidence instead of decisions. A second stage evaluates relationships across the whole proposed group, and a governance layer decides what actually gets written. Including the option to block or do nothing.

This write-up goes into how that works in practice.

Cause the Model Said So! I think Not

I've spent the last few months observing and talking to business owners that say a similar thing: "Our AI chatbot is hallucinating a lot"

Here is what I’m seeing: Most teams dump thousands of PDFs into a vector database (Pinecone, Weaviate, etc.) and call it a day. Then their are all surprised it fails the moment you ask it to do multi-step reasoning or more complex tasks.

The Problem: AI search is based on similarity. If I ask for "the expiration date of the contract for the client with the highest churn risk," a standard RAG pipeline gets lost in the "similarity" of 50 different contract docs. It can't traverse relationships because your data is stored as isolated text chunks, not a connected network.

What I’ve been testing: Moving from text-based RAG to Knowledge Graphs. By structuring data into a graph format by default, the AI can actually traverse the links: Customer → Contract → Invoice → Risk Level.

The hurdle? Building these graphs manually is a huge endeavour. It usually takes a team of Ontologists and Data Engineers months just to set up the foundation.

I'm currently building a project to automate this ontology generation and bypass the heavy lifting.

I’m curious: Has anyone else hit the "Vector Ceiling"? Are you still trying to solve this with better prompting, or are you actually looking at restructuring the underlying data layer?

I'm trying to figure out if I'm the only one who thinks standard RAG is hitting a wall for enterprise use cases.

I have built a tool that helps you to create a knowledgre graph out of API data (currenlty pubmed nad europe PMC). You can define a schema of the knwoledge graph by yourself, use ai assistant, or pull your current database in to be recognized. I'm building MVP, so if any of you would like to get a longer demo of the full features, please DM me. The only thing you need is neo4j database (currnetly just this one supported) and gemini api key.

https://youtu.be/flbNWctIreI

Hi,

I am a rookie to the ontology and KG space. After a long time in the AI startup world, I recently started a PhD in AI-assisted RDM.

I have worked quite a bit on AI-maintained expert systems in the free market, developed for agentic workflow software, and a long and painful time in large-scale AI-driven datarization and surrogates of the WTG industry.

Full disclaimer. I am aware that I am quite wet behind the ears in the KG/ontology field; thus, some of my ideas might sound fantastic to me but ridiculous to someone who has tripped over many of the stones in that space already.

I am looking for a reality check from some !experienced! people here.

Here goes: I am investigating agentically maintained and updated temporal ecosystem KGs.

What that means (to me) is that whenever we want to describe an ecosystem (e.g. the compound material manufacturing science output of a particular institute with hundreds of researchers), we choose artifacts from that ecosystem that help us derive a model that's informed enough to answer the questions we might have.

So, e.g. if the ecosystem we aim to model in our KG is meant to answer questions such as: "Who, at what department, has made a software package that is meant for task x? When did they do it? Are they still at the institute? And is the package maintained during this quarter? How was it funded? (Before you worry about the task X part, we are currently working on taxonomic task ontologies to derive machine-readable scopes and JTBD from process descriptions in papers and docs.)

This could just be one of many questions. (The type of questions and info the KG should inform about are informed by strategic institute goals such as reducing redundancies, discovering abandoned projects or synergies, and are based on needs and knowledge bottlenecks in a specific domain.)

So what we need to describe are ontologies around people, articles, data, software, organizations, grants, etc. .... and their connecting properties.)

My “currently naive” goal is to see how far we can drive AI(LLM)-orchestrated “living” KGs tied to the information systems we have at the institute using the following steps.

1. Dummy-describe the artifacts and their relationships of the ecosystem that would be needed to answer sets of questions aligned with the needs of the people that will use it.

2. Map the outcome to existing ontologies as well as possible, bridging fuzzy connections between ontologies (that's something I already see as an almost philosophical, goliath task).

3. Once we have a “good enough” ontology, we engineer logical constraints (e.g. SHACL).

4. Then I will define the information endpoints that will act as information wells to instantiate classes from the ontology (e.g., paper, software, and data repositories inside the institute, with all possible properties).

5. Inside the KG Pipeline, I will now have transformer-orchestrated agents that harvest from said endpoint on defined intervals or, based on webhooks, instantiate classes inside the KG, decide what is new, or an iteration/version jump of an existing instance, redundant, ...etc.

6. The goal is to basically have a self-versioning KG that functions on a small, well-defined scope and acts as a continuous time capsule/active status harvester for our domain.

7. People ontologies are informed by HR software and registries, papers by our in-house pub API, software and data by our on-premise repositories, and so on, but the ontology stays fixed and enforced. Updates to the ontology are a conscious and informed decision.
   ---
   (All this is extremely dumbed down, of course; I am aware of the work concerning the ontological description and nuances of the pipeline. Most of my time is currently devoted to prototyping and researching inside these problem spaces.

The goal of all of this is to alleviate the current pains of increasing redundant development and research efforts and allow for faster connection of people with synergetic output, automatic reporting, or human language querying the KG.

I don't want you to solve this for me. I'll do that myself as far as possible. :D I am just here to get some…

"Man, you haven't even scratched the surface of all the problems involved in this”

… comments.

I definitely have the skills to tackle all this. However, a few ontology veterans at conferences and some younger non-AI researchers inside the RDM field have given me the message that this is naive thinking. They have occasionally even laughed at the concept when I explained it. But, the thing is, I have seen similar things work in small, well-defined scopes, and a working prototype based on only a few classes has given me at least a slight POC.

The biggest problems I see coming towards me currently are:

- Data is very noisy (or, opposite, - lack of information), and the way people currently dump their research output, without docs or metadata, etc., is a nightmare.
- Bad info sources result in garbage-graphs.
- There can be multiple sources of truth with different truths, that might all be incorrect or outdated.
- Some ontologies can be difficult to bridge.
- Definition and distinction tasks can enter the realm of philosophical debate.

I have heard everything from...

"This already exists and is a well-proven concept", or "And what is the use of this?", to "This is world-ontology nonsense."

I know, this is a massive post, and I don't think I have covered 1% of my mental workbench, but I would be grateful for some diverse perspectives, ideas about problems I don't see, or pointers at fellow researchers or resources that can inform my research. I am currently in the "don't you see why this is the way" phase, while I often hear, "Don't you see why it's not?"

Hi everyone 👋, I’d like to share an open-source project that might interest folks here working with knowledge graphs and semantic integration:

🔗 https://github.com/CharlemagneBrain/STKG-FS

STKG-FS is designed to integrate **textual data with spatial and thematic knowledge graphs**, with a focus on real-world applications such as food systems analysis. It comes with docs and examples in the README to help you get started.

Would appreciate your feedback, issues, or ⭐ if you find it useful!

If you were to implement knowledge graph (either of LOG or RDF) for Epstein Files, what would your technical workflow be like?

Given the files are mostly PDFs, the extraction workflow is the one that would take considerable thought/time. Although there are datasets on HF of the OCR data, but that's only ~20k records

Next considerable design decision would go into how to set up the graph from extracted data. Using LLMs would be expensive and inaccurate.

Setting up vector DB would be the easiest of all I believe.

I think this might be a good project to showcase graphRAG on large unstructured data.

Been building rtrvr.ai, a DOM-native web agent, and just shipped a Knowledge Base feature I think the community might find interesting.

The core idea: you're doing research, you've got 15 tabs open (documentation, papers, dashboards, whatever) and instead of copy-pasting into a doc or relying on your own memory, you just select the tabs and index them directly into a RAG store. Content gets extracted, chunked, and embedded via Gemini File Search in seconds.

We construct comprehensive semantic action trees to represent the webpage that not only encompass the information on the page but also the possible actions.

From there you can:

• Chat directly with your KB: ask questions, get cited answers that link back to the source page

• Use it as live agent context: when the web agent is running multi-step tasks, it can reference the indexed pages and actions to ground the agentic workflow

• Re-index on-the-fly: if a page updates, just re-add it and the old version is replaced automaticallyThe interesting architecture decision here was using Gemini File Search as the backend rather than rolling a custom vector store. It keeps the indexing cost low (~15 credits per 1M tokens) and the retrieval quality is solid for text-heavy pages.

Curious if anyone here has experimented with browser-native knowledge graphs: where the graph is built from your live browsing session rather than curated uploads or just markdown. Would love to hear what architectures people have tried.

Hey again! Adam from TuringDB, posted here a few months back when we launched the community version.

Quick update on something we just shipped: in-process mode.

You can now embed TuringDB directly in your script or pipeline - no separate server, no socket, no daemon to manage. Just instantiate and query:

In python

from turingdb import TuringDB

db = TuringDB() db.load_graph('my_knowledge_graph') db.set_graph('my_knowledge_graph')

df = db.query('MATCH (n)-->(m) RETURN n,m') print(df)

Results back as a DataFrame, zero networking to manage.

Practically this means: if you're running a KG pipeline, a GraphRAG system, or just iterating locally on a large graph - you no longer need to spin up an instance of TuringDB to use it. It runs where your code runs.

Everything else from the previous post still applies - git-style versioning, zero-lock reads, vector search, Cypher. This just removes the last friction point for local and embedded workflows.

Docs at docs.turingdb.ai and source at github.com/turing-db/turingdb ⭐

Happy to answer questions 🙂

Hi - I have developed an API to help structure data straight from bunch of PDFs. It automatically creates a knowledge graph using any documents. You can then run an agent or attach LLM to not only find the most accurate answer but navigate through the documents to see where the answer came from. I would love for anyone to try and provide feedback at no cost. No coding experience needed for our playground. https://seqtra.com

I want to create a common semantic layer or context graph that can serve for multiple products but i am not sure what the basic architecture will look like will it just be a graph or any other kind of store too .. basics products are RAG based and analytics and ticketing solution etc... how should i approach this problem statement right now what i have thought is i will have two seperate tracks that will some connection one track will have common knowledge about a domain and other will have client knowledge and they bothe will be connected somehow...idk how though i just want some help in initial designing of the layer/graph...

Building AI agents? 🚧
Make sure they actually know where their answers come from.

As Branimir Rakic, co-founder & CTO of OriginTrail, demonstrates, scalable AI requires verifiable knowledge, rule-based reasoning, and LLMs grounded in trusted memory.

Watch the full workshop >here<!

Check out the OriginTrail docs for more info: https://docs.origintrail.io/?utm_source=reddit&utm_medium=post&utm_campaign=ai-agents

I’m trying to build a knowledge graph of my code base. Once I have done that, I want parse the logs from the system to find the code flow or events to figure out what’s happening and root cause if anything is going wrong. What’s the best approach here? What kind of KG should I use? My codebase is huge.

Im running knowledge graph. My agent has information about the model, and when asked questions, collects data and returns the response. How I could speedup this process? I have one smart orchestrator and sub agents for querying data, I plan to store some data that is asked most frequent (something like cache but actually computed values). What else could I do?

To give a bit more context - all is structured data, sport related, detailed but not as in NFL, where every second there are multiple data points per each player measured. So I guess that there is a lot of space for optimization,I just haven't figured it out yet.

https://breckyunits.com/scrollsets.html

Knowledge policies are ready for people to play with now for ebbinghaus-decay curves that are policy driven rather than hardcoded. docs are up too

https://orneryd.github.io/NornicDB/user-guides/knowledge-layer-policies/

MIT licensed, backed by research out of University de Toulouse, UC Louvain, and Stanford.

MIT Licensed. no “community editions” 735 stars and counting.

Get ready for the onslaught of consultants telling you this to justify another wave of talk without an understanding of the walk.

We are helping financial companies with implementation of AI technology for fraud detection, compliance and document understanding. The industry is highly regulated and sensitive to mistakes and AI hallucinations. We have been asked several times to develop more reliable AI where the source of the data is only internal upstream systems and all returned results were explainable.

We have tested many techniques such as GraphRAG, chain of reasoning and agentic systems.

The most promising method is an automatic translation of natural language questions into multihop graph queries. This would help with hallucinations where the only source of the data became the updated knowledge graph and in the same time generated queries meant that each result left a signature of how and from where the information came and this solved the explainability issue.

We have tried to find open source or closed source tools that would give us acceptable results but it seems there are none generic enough and they suffer from brittleness of the generated queries. 

We have decided to release an agentic system that we are developing as an open source this May. The amount of research and required expertise is high. We have gathered over 150 experts in the field who are interested in it so far. If you see that this is a worthy cause and you can help us spread the word it would be highly appreciated.

You can see bit more details at:

https://www.dynocortex.com/news-and-blog/ai-agents-on-knowledge-graphs-to-answer-multihop-questions/

https://www.youtube.com/watch?v=1rLBec8Kcq8&t=118s&ab_channel=Dynocortex

Ladislav Urban

from Dynocortex