Knowledge graphs for LLM grounding and avoiding hallucination
This blog post is part of a series that dives into various aspects of SAP’s approach to Generative AI, and its technical underpinnings. In previous blog posts of this series, you learned about how to use large language models (LLMs) for developing AI applications in a trustworthy and reliable manner...
Enabling LLM development through knowledge graph visualization
Discover how to empower LLM development through effective knowledge graph visualization. Learn to leverage yFiles for intuitive, interactive diagrams that simplify debugging and optimization in AI applications.
"Knowledge Graphs Applied" becomes "Knowledge Graphs and LLMs in Action"
🎉🎉 🎉 "Knowledge Graphs Applied" becomes "Knowledge Graphs and LLMs in Action"
Four years ago, we embarked on writing "Knowledge Graphs Applied" with a clear mission: to guide practitioners in implementing production-ready knowledge graph solutions. Drawing from our extensive field experience across multiple domains, we aimed to share battle-tested best practices that transcend basic use cases.
Like fine wine, ideas, and concepts need time to mature. During these four years of careful development, we witnessed a seismic shift in the technological landscape. Large Language Models (LLMs) emerged not just as a buzzword, but as a transformative force that naturally converged with knowledge graphs.
This synergy unlocked new possibilities, particularly in simplifying complex tasks like unstructured data ingestion and knowledge graph-based question-answering.
We couldn't ignore this technological disruption. Instead, we embraced it, incorporating our hands-on experience in combining LLMs with graph technologies. The result is "Knowledge Graphs and LLMs in Action" – a thoroughly revised work with new chapters and an expanded scope.
Yet our fundamental goal remains unchanged: to empower you to harness the full potential of knowledge graphs, now enhanced by their increasingly natural companion, LLMs. This book represents the culmination of a journey that evolved alongside the technology itself. It delivers practical, production-focused guidance for the modern era, in which knowledge graphs and LLMs work in concert.
Now available in MEAP, with new LLMs-focused chapters ready to be published.
#llms #knowledgegraph #graphdatascience
"Knowledge Graphs Applied" becomes "Knowledge Graphs and LLMs in Action"
Build your hybrid-Graph for RAG & GraphRAG applications using the power of NLP | LinkedIn
Build a graph for RAG application for a price of a chocolate bar! What is GraphRAG for you? What is GraphRAG? What does GraphRAG mean from your perspective? What if you could have a standard RAG and a GraphRAG as a combi-package, with just a query switch? The fact is, there is no concrete, universal
A comparison between ChatGPT and DeepSeek capabilities writing a valid Cypher query
Today, I conducted a comparison between ChatGPT and DeepSeek chat capabilities by providing them with a schema and a natural language question. I tasked them…
a comparison between ChatGPT and DeepSeek chat capabilities by providing them with a schema and a natural language question. I tasked them with writing a valid Cypher query to answer the question.
Terminology Augmented Generation (TAG)? Recently some fellow terminologists have proposed the new term "Terminology-Augmented Generation (TAG)" to refer to… | 29 comments on LinkedIn
What is really Graph RAG? Inspired by "From Local to Global: A Graph RAG Approach to Query-Focused Summarization" paper from Microsoft! How do you combine… | 12 comments on LinkedIn
A zero-hallucination AI chatbot that answered over 10000 questions of students at the University of Chicago using GraphRAG
UChicago Genie is now open source! How we built a zero-hallucination AI chatbot that answered over 10000 questions of students at the University of… | 25 comments on LinkedIn
a zero-hallucination AI chatbot that answered over 10000 questions of students at the University of Chicago
Enhancing RAG-based apps by constructing and leveraging knowledge graphs with open-source LLMs
Graph Retrieval Augmented Generation (Graph RAG) is emerging as a powerful addition to traditional vector search retrieval methods. Graphs are great at repre...
The journey towards a knowledge graph for generative AI
While retrieval-augmented generation is effective for simpler queries, advanced reasoning questions require deeper connections between information that exist across documents. They require a knowledge graph.
Improving Retrieval Augmented Generation accuracy with GraphRAG | Amazon Web Services
Lettria, an AWS Partner, demonstrated that integrating graph-based structures into RAG workflows improves answer precision by up to 35% compared to vector-only retrieval methods. In this post, we explore why GraphRAG is more comprehensive and explainable than vector RAG alone, and how you can use this approach using AWS services and Lettria.
We're excited to publicly release the Diffbot GraphRAG LLM! With larger and larger frontier LLMs, we realized that they would eventually hit a limit in terms… | 48 comments on LinkedIn
Ontologies and knowledge graphs are the secret sauce for AI
𝐌𝐲 𝐛𝐨𝐥𝐝 𝐚𝐧𝐝 𝐨𝐧𝐥𝐲 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝟐𝟎𝟐𝟓: By December, everyone, their chatbot, and their agents will finally agree that ontologies… | 80 comments on LinkedIn
ontologies and knowledge graphs are the secret sauce for AI
