Synergizing LLMs and KGs in the GenAI Landscape | LinkedIn
Our paper "Are Large Language Models a Good Replacement of Taxonomies?" was just accepted to VLDB'2024! This finished our last stroke of study on how knowledgeable LLMs are and confirmed our recommendation for the next generation of KGs. How knowledgeable are LLMs? 1.
GitHub - zazuko/blueprint: Zazuko Blueprint is an enterprise knowledge graph frontend and browser, designed to make RDF Knowledge Graphs accessible and customizable for domain users.
Zazuko Blueprint is an enterprise knowledge graph frontend and browser, designed to make RDF Knowledge Graphs accessible and customizable for domain users. - zazuko/blueprint
GraCoRe: Benchmarking Graph Comprehension and Complex Reasoning in Large Language Models
Can LLMs understand graphs? The results might surprise you. Graphs are everywhere, from social networks to biological pathways. As AI systems become more…
GraCoRe: Benchmarking Graph Comprehension and Complex Reasoning in Large Language Models
PDF | BIFROST is a novel query engine for graph databases that supports high-fidelity data modeling on arbitrary and evolving graph topologies. It... | Find, read and cite all the research you need on ResearchGate
What Could Go Wrong When We Start Using LLMs to Organize Knowledge? 7 Pain Points of GraphRAG Alright, tech enthusiasts and AI aficionados. We need to discuss… | 43 comments on LinkedIn
Every time I write about why graph-based RAG produces more insightful and more accurate answers for Q&A / digital assistant AI applications, people ask — do… | 21 comments on LinkedIn
If you, like me, aspire to create your knowledge graph on Google, I have two recommendations for you: 1º Use the tool at demo.nl.diffbot.com to visualize the…
GraphRAG: New tool for complex data discovery now on GitHub
GraphRAG, a graph-based approach to retrieval-augmented generation (RAG) that significantly improves question-answering over private or previously unseen datasets, is now available on GitHub. Learn more:
Associating unstructured data with structured information is crucial for real-world tasks that require relevance search. However, existing graph learning benchmarks often overlook the rich...
The Zazuko Knowledge Graph Forum serves as a platform where companies are invited to share their ongoing work and use cases with Knowledge Graphs. Our goal i...
“I knew I shoulda’ taken that left turn at Albuquerque.” – Bugs Bunny For better or worse, much of my childhood was informed by Looney Tunes, Monty Python, and a diet of science fiction rangi…
GraphReader: Long-Context Processing in AI ... As AI systems tackle increasingly complex tasks, the ability to effectively process and reason over long…
Large Generative Models (LGMs) such as GPT, Stable Diffusion, Sora, and Suno are trained on a huge amount of language corpus, images, videos, and audio that are extremely diverse from numerous...
How to develop a Graph Foundation Model (GFM) that benefits from large-scale training with better generalization across different domains and tasks
💡 How to develop a Graph Foundation Model (GFM) that benefits from large-scale training with better generalization across different domains and tasks? 🔎…
Upgrade your RAG applications with the power of knowledge graphs./b
Retrieval Augmented Generation (RAG) is a great way to harness the power of generative AI for information not contained in a LLM’s training data and to avoid depending on LLM for factual information. However, RAG only works when you can quickly identify and supply the most relevant context to your LLM. Knowledge Graph-Enhanced RAG/i shows you how to use knowledge graphs to model your RAG data and deliver better performance, accuracy, traceability, and completeness.
Inside Knowledge Graph-Enhanced RAG/i you’ll learn:
The benefits of using Knowledge Graphs in a RAG system/li
How to implement a GraphRAG system from scratch/li
The process of building a fully working production RAG system/li
Constructing knowledge graphs using LLMs/li
Evaluating performance of a RAG pipeline/li
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Knowledge Graph-Enhanced RAG/i is a practical guide to empowering LLMs with RAG. You’ll learn to deliver vector similarity-based approaches to find relevant information, as well as work with semantic layers, and generate Cypher statements to retrieve data from a knowledge graph.
DiffKG: Knowledge Graph Diffusion Model for Recommendation
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DiffKG: Knowledge Graph Diffusion Model for Recommendation
GraphGeeks Talk Ep6: RDF vs Property Graphs: Ask Me Anything
Graphs are changing the way we model, store, and query complex data. But when it comes to choosing the right type of graph model, the decision often boils do...
