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What are the concepts that have the most influence in language? For anyone that missed this fun language model + network science community talk, here's the…

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

Gemini 1.5 is so powerful that it can read all the Harry Potter books at once and generate a graph of the characters with perfect accuracy. All the books have… | 146 comments on LinkedIn

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…

The International Standards Organization’s publication of a standard for the Graph Query Language earlier this month generated relatively little media interest, but executives at graph database make

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.

Lots of gratifying announcements about the GQL standard: Neo4j, TigerGraph, JTC 1, AWS/Neo4j, Memgraph, Stefan the editor, The Register ..

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…

Optimizing vector retrieval with advanced graph-based metadata techniques using LangChain and Neo4j Editor's Note: the following is a guest blog post from Tomaz Bratanic, who focuses on Graph ML and GenAI research at Neo4j. Neo4j is a graph database and analytics company which helps organizations find hidden relationships and patterns

Read the joint letter from Neo4j and AWS to graph customers, the graph curious, and the Cypher community on the new GQL standard.

Explore Altair's latest news releases for cutting-edge innovations, collaborations, and industry insights driving technological advancements and progress.

Standards body adoption could help ease portability between vendors

Free diagram visualization extension for JupyterLab and Jupyter Notebook.

Ultipa Graph, one of the newer entrants to the graph database space, is approaching its 5.0 version update.

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…

📢 How to apply Graph Neural Networks to stock predictions? The following paper presents the ideal starting point ….keep reading! 👇 This paper proposes 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.

A survey of the current methods of integration

cognee: Train your knowledge graph generation and search with DSPy, Weaviate, and Neo4j to generate deterministic LLMs outputs! GitHub…