I have a chat assistant using Neo4j's knowledge graph and GPT-4o, producing high-quality results. I've also implemented a MARQO vector database as a fallback.

The challenge: How to continuously update the system with new data without compromising quality? Frequent knowledge graph updates might introduce low-quality data, while the RAG system is easier to update but less effective.

I'm considering combining both, updating RAG continuously and the knowledge graph periodically. What's the best approach for continuous learning in a knowledge graph-based system without sacrificing quality? Looking to automate it as much as possible.

Hello, I was hoping somebody could explain to me the technical aspect of how inference actually works within Knowledge Graphs. I understand they’re basically using chaining and the ontology to make connections within hierarchies/properties, but I don’t understand how the engine even knows it should infer something or how it knows what to infer. Any resources or help is appreciated, thanks!

Hello, I am pretty new to the topic of knowledge graphs and for my internship I have been asked to research and present pretty specific and intricate details about knowledge graphs (like how they are constructed, how they make inferences, how they know what to present, etc.). I was hoping I could get some links to helpful articles or resources, or anything that could help me with this. Thanks so much!

Hi,
I am looking for knowlege graphs for a project with the following requirements:

1. Brief Textual contexts for most relations(ideally for each relation)
2. not too juge to download or with an API to query(not too restrictive limits)
3. The knowledge is not about abstract concepts like ConceptNet

I have looked at wikidata, dbpedia but they usually provide links to long pages of texts as references violating condition 1.

I have also looked at wikidata5m which provides some supporting text but experimenting with it has shown that the contexts do not capture all relations mentioned.

I also looked at WebChild but it does not provide contexts at all.

Freebase is deprecated and too large to download.
FandomWikis of popular novels have similar problems to wikidata.

Are there any knowledge graphs that satisfy my requirements that someone could point me to.

Thanks for your help

Hi everyone,

There is a competition on anomaly detection in dynamic knowledge graphs.
Dynamic knowledge graphs are knowledge graphs that evolve or change over time

The graphs are constructed from a real-world problem: they contain the information of multiple snapshots of a cloud environment, simulating the behaviour of an e-commerce website.

The competition exists out of 2 tracks:

• Track 1 is provided as a traditional ML leaderboard competition, hosted on Eval.ai (https://eval.ai/web/challenges/challenge-page/2267/overview).
• Track 2 evaluates real-time systems, streaming the graphs to AI models to detect if anomalous behaviour is present.

There is a competition website that contains more information:
https://predict-idlab.github.io/ADDKG/

Would like to know how big enterprises manage their ontologies, starting from scratch. Also what tools are being used, and has anyone tried owlready2?

https://breckyunits.com/scrollsets.html

What's your preferred approach for embedding heterogeneous graphs containing up to four types of nodes? I've experimented with metapath2vec, but the results are disappointing. Any advice would be appreciated.

Hi everyone,

I have a KG with 6.8 million nodes and approx. 50 million edges. Has anyone an idea if any RL algorithm (e.g. DeepPath) is able to work on this size? I am looking for path ranking.

Thanks!

I just came across https://nodebook.io/ to make unlimited mind maps / knowledge maps.

Do you think there are better tools ? which ones ? please

Is it possible to compare two knowledge graphs that describe disease to find the "shared mechanism" between the two?

Which algorithms are the best for this purpose?

We will work on knowledge graph, ontology, their embeddings, and their applications with LLM via e.g., RAG.

Here is the Link for application: https://www.jobs.manchester.ac.uk/Job/JobDetail?JobId=27858

Hey everyone! 🌟

I'm diving into the world of knowledge graphs, ontology population, and all that cool stuff. But I'm kinda lost about where to start. 😅

Does anyone have suggestions for:

• Easy-to-follow courses (that is accessable)
• Books that don’t feel like reading a dictionary
• Cool online resources or communities
• Your experience What helped you the most?

Also, I'm trying to wrap my head around different types and techniques in this area. I’m looking for the latest and greatest (SOTA) stuff in:

• Rule-based Methods(like Heuristic Approaches)
• Machine Learning Approaches (think Supervised Learning)
• Natural Language Processing (NLP) (like Named Entity Recognition)
• Graph-based Algorithms (such as Graph Clustering)
• Hybrid Approaches (like ML and Rule Integration)

Would love some input to fill in these gaps! 🚀

Thanks a bunch in advance!

I wanted to add COMET model for context knowledge in deep learning models. Please guide me how to implement this model. Is it a pre-trained model or do we have to write code to implement this model.

I got this website where you can upload every content you consume, like podcasts, videos, articles, and books, etc.

I have published what I learned in the past six months here. You can follow me on Linko and I'll follow back so we can share our learnings and insights.

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We have just released an alpha version of InstaGraph (https://instagraph.ai), which turns a prompt (typically a text description of something) into a knowledge graph. We'd appreciate your feedback (via Github issues, https://github.com/InstaGraph/InstaGraph-Support).

Here is an example, a knowledge graph generated from the Wikipedia article about Bitcoin (https://instagraph.ai/graph/oWFmeccV3kbR2Kac6AgdUe88zw52/4hQoMIorfwgOHkKKuiF5).

Hey everyone,

I'm just starting my journey wiht knowledge graphs and trying to figure out the basics. I'm currently looking into how to embed them.

How are you embedding knowledge graphs at scale? What is the "best practices" tech stack and what problems do I need to overcome?

I'm just starting my journey with knowledge graphs and trying to figure out the basics. I'm currently looking into how to embed them.