Graphs + Transformers = the best of both worlds ๐ค The same models powering breakthroughs in natural language processing are now being adapted for graphsโฆ
GNN: Graph Neural Network and Large Language Model Based for Data Discovery
Our algorithm GNN: Graph Neural Network and Large Language Model Based for Data Discovery inherits the benefits of [Hoang(2024b)] (PLOD: Predictive Learning Opt
A Graph Neural Network (GNN) won the highly competitive Causal Discovery competition arranged by ADIA Lab
A Graph Neural Network (GNN) won the highly competitive Causal Discovery competition arranged by ADIA Lab. Of course I mean to say that Hicham Hallak won theโฆ | 19 comments on LinkedIn
A Graph Neural Network (GNN) won the highly competitive Causal Discovery competition arranged by ADIA Lab
Enterprise GraphRAG: Building Production-Grade LLM Applications with Knowledge Graphs
Enterprise GraphRAG: Building Production-Grade LLM Applications with Knowledge Graphs Letโs dive into the numbers: Real-World Results Implementing GraphRAGโฆ
Enterprise GraphRAG: Building Production-Grade LLM Applications with Knowledge Graphs
For newcomers into the world of semantic technology and knowledge graphs, the diagram above illustrates some of the key languages that you may want to look into. RDF RDF defines the very lowest level building blocks of how graphs can be represented.
A collection of Graph Embedding methods in Python. ๐ง ๐ This repository provides hands-on implementations of essential graph embedding algorithms like: โช๏ธโฆ
Wonderful debate here. Letโs set the record straightโif youโre still stuck thinking schema markup is just a checkbox for rich results, youโre not only missing the point but also losing the game.
Want better results from your RAG? GraphRAG takes it to the next level. GraphRAG is a powerful approach to retrieval augmented generation (RAG). Itโฆ | 46 comments on LinkedIn
LazyGraphRAG sets a new standard for GraphRAG quality and cost
Introducing a new approach to graph-enabled RAG. LazyGraphRAG needs no prior summarization of source data, avoiding prohibitive up-front indexing costs. Itโs inherently scalable in cost and quality across multiple methods and search mechanisms:
why graphs would be superior to using Python for agents
Graph is increasingly driving the Agentic space, which I see as being a very good sign. Recently, a programmer asked why graphs would be superior to usingโฆ
Label Property Graphs (LPGs) are not ontological-based knowledge graphs because they lack the formal semantics and logical rigor that underpin ontologies
Dear LinkedIn Fam, We need to have a conversation about somethingโฆ Label Property Graphs (LPGs) are not ontological-based knowledge graphs because they lackโฆ
Label Property Graphs (LPGs) are not ontological-based knowledge graphs because they lack the formal semantics and logical rigor that underpin ontologies
Paco Nathan's Graph Power Hour: Understanding Graph Rag
Watch the first podcast of Paco Nathan's Graph Power Hour. This week's topic - Understanding Graph Rag: Enhancing LLM Applications Through Knowledge Graphs.
The Power of Graph-Native Intelligence for Agentic AI Systems
The Power of Graph-Native Intelligence for Agentic AI Systems How Entity Resolution, Knowledge Fusion, and Extension Frameworks Transform Enterprise AI โกโฆ
The Power of Graph-Native Intelligence for Agentic AI Systems
Knowledge Graph/Ontologies practical lessons for managers
I want to emphasize some things that most people don't seem to understand, specially managers in the AI space. 1. Knowledge Graph/Ontologies without a way toโฆ | 14 comments on LinkedIn
Working with RDF on LLMs ================== The following is a quick reference list of things I've found when trying to work with the RDF stack on LLMs *โฆ | 14 comments on LinkedIn
Unlocking universal reasoning across knowledge graphs
Unlocking universal reasoning across knowledge graphs. Knowledge graphs (KGs) are powerful tools for organizing and reasoning over vast amounts ofโฆ | 11 comments on LinkedIn
Unlocking universal reasoning across knowledge graphs.
Samsung and Appleโs knowledge-centric approaches to secure, personalized AI on phones - DataScienceCentral.com
Image by David from Pixabay Mobile phones make it possible to secure and manage personal data on-device, which opens up a novel opportunity for both phone owners and device manufacturers: AI personalization via a data resource that stays on the phone. With the right design, personal knowledge graph on-device could provide contextualization while at theโฆย Read More ยปSamsung and Appleโs knowledge-centric approaches to secure, personalized AI on phones
A New Era of Graph-Based Security Accelerated by AI | LinkedIn
At Microsoft, we strive to constantly learn and share our insights across innovation, culture and governance. Today, as we reflect on our progress since launching the Secure Future Initiative one year ago, I want to share a little more about our vision for a graph-powered platform future.
takeaways from the International Semantic Web Conference #iswc2024
My takeaways from the International Semantic Web Conference #iswc2024 Ioana keynote: Great example of data integration for journalism, highlighting the use ofโฆ
takeaways from the International Semantic Web Conference hashtag#iswc2024
I'm coming around to the idea of ontologies. My experience with entity extraction with LLMs has been inconsistent at best. Even running the same request withโฆ | 63 comments on LinkedIn
Beyond Vector Space : Knowledge Graphs and the New Frontier of Agentic System Accuracy
Beyond Vector Space : Knowledge Graphs and the New Frontier of Agentic System Accuracy โณ In the realm of agentic systems, a fundamental challenge emergesโฆ
Beyond Vector Space : Knowledge Graphs and the New Frontier of Agentic System Accuracy
๐ Iโve been out of the grid the past few weeks to wrap up my upcoming book. ๐ After 12 months of work that turned out to be more intense than I expectedโฆ | 20 comments on LinkedIn
