This month makes it 2 years since I was featured as a guest on a @neo4j YouTube livestream alongside @alexandererdl, where I discussed & demoed my analysis of the FIFA22 dataset. You can watch it here, in case you didn't see the live event 2 years ago 😉
#neo4j #FIFA22 #dataviz #graphdatabase #graphDataScience
https://www.youtube.com/live/ZADwMoBJ6GQ?si=VG_HnFCvZRdYGH1h
Lately I’ve become intrigued about the published research + open source code for a relatively specific topic: generating graphs to use for inference.
Here is a comparison of five research projects circa 2019-2024 which explore different ways of generating graphs to use for inference.
"Graph Levels of Detail"
https://blog.derwen.ai/graph-levels-of-detail-ea4226abba55
We're circulating for review this survey of methods for abstraction layers in knowledge graphs. This covers mathematical approaches from several areas, so it's important to hear back whether any of the descriptions are misrepresented. Also, is this kind of work interesting for your organization?
Here are slides for my recent talk -- I really appreciated the opportunity to present at K1st World, JK2K's meetup in DC, and Corunna Innovation Summit, and the many interesting discussions!
"Language, Graphs, and AI in Industry"
https://derwen.ai/s/mqqm
This links to a directory of resources related to graph resources:
https://derwen.ai/graph/
Also, join our Graph Data Science group on LinkedIn:
https://www.linkedin.com/groups/6725785/
Over the past 5 years, news about AI has been filled with amazing research – at first focused on graph neural networks (GNNs) and more recently about large language models (LLMs). Understand that business tends to use connected data – networks, graphs – whether you’re untangling supply networks in Manufacturing, working on drug discovery for Pharma, or mitigating fraud in Finance. Starting from supplier agreements, bill of materials, internal process docs, sales contracts, etc., there’s a graph inside nearly every business process, one that is defined by language. This talk addresses how to leverage both natural language and graph technologies together for AI applications in industry. We’ll look at how LLMs get used to build and augment graphs, and conversely how graph data gets used to ground LLMs for generative AI use cases in industry – where a kind of “virtuous cycle” is emerging for feedback loops based on graph data. Our team has been engaged, on the one hand, with enterprise use cases in manufacturing. On the other hand we’ve worked as intermediaries between research teams funded by enterprise and open source projects needed by enterprise – particularly in the open source ecosystem for AI models. Also, there are caveats; this work is not simple. Translating from latest research into production-ready code is especially complex and expensive. Let’s examine caveats which other teams should understand, and look toward practical examples.
A summary of links for the emerging intersection of graph ML and uses for LLMs
https://blog.derwen.ai/visual-missives-from-the-latent-space-2023-10-16-d4bfa944b86c
#TheDataExchangePod 🎧 Emil Eifrem of Neo4j unlocks the secrets of #GraphDatabases, #LLMs, #VectorDatabases, and more. We examine the interplay of these elements with knowledge graphs and applications of graph neural networks. We explore the rise of new database companies and delve into the world of #GraphDataScience & retrieval-augmented LLMs
#nlproc #machinelearning #generativeai
🔗 https://thedataexchange.media/the-future-of-graph-databases/
#TheDataExchangePod 🎧 Emil Eifrem of Neo4j unlocks the secrets of #GraphDatabases, #LLMs, #VectorDatabases, and more. We examine the interplay of these elements with knowledge graphs and applications of graph neural networks. We explore the rise of new database companies and delve into the world of #GraphDataScience & retrieval-augmented LLMs
#nlproc #machinelearning #generativeai
🔗 https://thedataexchange.media/the-future-of-graph-databases/
#TheDataExchangePod 🎧 Emil Eifrem of Neo4j unlocks the secrets of #GraphDatabases, #LLMs, #VectorDatabases, and more. We examine the interplay of these elements with knowledge graphs and applications of graph neural networks. We explore the rise of new database companies and delve into the world of #GraphDataScience & retrieval-augmented LLMs
#nlproc #machinelearning #generativeai
🔗 https://thedataexchange.media/the-future-of-graph-databases/
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