Super cool to see how Gemini uses Graph RAG to create travel plans. Check out this 60 second video. Graphs are everywhere. Emil Eifrem Alyson Welch Chandra…

A Knowledge Graph Forum 2023 presentation by Krasimira Bozhanova, Solutions Architect at Ontotext.

Watch my colleague Jonathan Larson present on GraphRAG!GraphRAG is a research project from Microsoft exploring the use of knowledge graphs and large language...

"Add Semantic Data Integration & Knowledge Graphs" is one of the Top 10 trends in Data Integration and Engineering, presented by Mark Beyer at Gartner D&A…

Learn how to create knowledge graphs easily by turning PDF documents into graph models using LlamaParse for better RAG applications.

Had a great time at The Knowledge Graph Conference last week! Here are my takeaways: Not surprisingly, there was a ton of presentations and talk about GenAI…

Knowledge Graphs combined with modern language models is still a hot topic. We have seen many demos in the past, and increasingly, they have become features in products, but most of the time, they are

I see a growing consensus: more and more people are catching on to the value of knowledge graphs. I hear fewer and fewer questions this year about the basic concepts of knowledge graphs as fancy databases.

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While LLMs are adept at pattern recognition and generating human-like text, they often struggle with maintaining accuracy over time and…

Symbolica's groundbreaking AI approach using advanced math promises human-like reasoning, unparalleled transparency, and efficiency in a fraction of the data.

If powerful LLMs were all that was needed to get to great enterprise generative AI programs, then hundreds of thousands of open-source and closed-source LLMs… | 46 comments on LinkedIn

An open-source library for building knowledge graphs from text corpus using open-source LLMs like Llama 3 and Mixtral.

🌐 Announcement: Join Us at the Knowledge Graph Semantic Web Conference! 🌐 Dear colleagues and enthusiasts of the Semantic Web, We are thrilled to extend a…

Use this year’s Gartner Emerging Tech Impact Radar to: ☑️Enhance your competitive edge in the smart world ☑️Prioritize prevalent and impactful GenAI use cases that already deliver real value to users ☑️Balance stimulating growth and mitigating risk ☑️Identify relevant emerging technologies that support your strategic product roadmap Explore all 30 technologies and trends: www.gartner.com/en/articles/30-emerging-technologies-that-will-guide-your-business-decisions

With the rise of LLMs, we can now store both structure and semantic meaning as vector embeddings in knowledge graphs.

Global Reasoning with Knowledge Graphs and Generative AI ⏺ Large language models (LLMs) open in a new era of natural language processing…

Introducing Microsoft Graph RAG: Enhancing AI's Ability to Summarize Large Text Corpora ... 👉A New Approach to Query-Focused Summarization based on Knowledge… | 21 comments on LinkedIn

DoorDash leverages Large Language Models to build a product knowledge graph that captures relationships between millions of consumer goods.

When to use graph data models, such as parent-child, question-based, and topic-summary, for RAG applications powered by knowledge graphs.

Integrating temporal data into static knowledge graphs

Language models have achieved impressive performances on dialogue generation tasks. However, when generating responses for a conversation that requires factual knowledge, they are far from perfect, due to an absence of mechanisms to retrieve, encode, and reflect the knowledge in the generated responses. Some knowledge-grounded dialogue generation methods tackle this problem by leveraging facts from Knowledge Graphs (KGs); however, they do not guarantee that the model utilizes a relevant piece of knowledge from the KG. To overcome this limitation, we propose SUbgraph Retrieval-augmented GEneration (SURGE), a framework for generating context-relevant and knowledge-grounded dialogues with the KG. Specifically, our SURGE framework first retrieves the relevant subgraph from the KG, and then enforces consistency across facts by perturbing their word embeddings conditioned by the retrieved subgraph. Then, we utilize contrastive learning to ensure that the generated texts have high similarity to the retrieved subgraphs. We validate our SURGE framework on OpendialKG and KOMODIS datasets, showing that it generates high-quality dialogues that faithfully reflect the knowledge from KG.

Have you tried Croissant? If not, you are missing out. Using LLMs to generate knowledge graphs is an exciting area of exploration. My colleague Jesús Barrasa…

AI has been a part of healthcare and life sciences for decades. You might remember hearing about the very first chatbot, ELIZA, created at MIT in 1964 by Joseph Weizenbaum to explore communication between machines and humans.

💥 Breaking News: #ECLASS as #RDF is now a reality! 😎 🎉 By leveraging RDF serialization, ECLASS is now poised to revolutionize #data #interoperability and…

Interested in the world of semantic web? Then maybe you stumbled on the right article this time. I will be talking about the…

Learn how the 2023 Gartner Hype Cycle™ identifies exciting innovations in artificial intelligence that will be beneficial to business.

Our paper "𝐺𝑟𝑎𝑝ℎ𝐸𝑅: 𝐴 𝑆𝑡𝑟𝑢𝑐𝑡𝑢𝑟𝑒-𝑎𝑤𝑎𝑟𝑒 𝑇𝑒𝑥𝑡-𝑡𝑜-𝐺𝑟𝑎𝑝ℎ 𝑀𝑜𝑑𝑒𝑙 𝑓𝑜𝑟 𝐸𝑛𝑡𝑖𝑡𝑦 𝑎𝑛𝑑 𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛… | 34 comments on LinkedIn

Learn how to use Neo4j and Senzing to build entity resolved knowledge graphs that remove duplicate data and improve analytics accuracy.