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A practical guide to constructing and retrieving information from knowledge graphs in RAG applications with Neo4j and LangChain

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Are you tired of manually tracing package vulnerabilities across applications? The #AmazonNeptune team has a solution! As part of the newest release of the…

Learn more about what graph embeddings are and how they're used to accelerate real-time analytics. Find out about graph embedding algorithms and storage.

𝐔𝐧𝐥𝐞𝐚𝐬𝐡𝐢𝐧𝐠 𝐭𝐡𝐞 𝐏𝐨𝐰𝐞𝐫 𝐨𝐟 𝐆𝐫𝐚𝐩𝐡 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬: 𝟐𝟓 𝐓𝐨𝐩 𝐏𝐲𝐭𝐡𝐨𝐧 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬, 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬, 𝐓𝐲𝐩𝐞𝐬 𝐚𝐧𝐝… | 27 comments on LinkedIn

The next frontier of LLM applications: Structured Knowledge Representation

In this article we look at Research Graph as an information model , and an approach to connect and capture the connections between research outputs, researchers and research activities. We explore…

Unlocking the power of knowledge graphs in research catalogues

Harvesting the Power and Knowledge of Large Language Models

Code for all my videos: https://github.com/sponsors/adumb-codes/Twitter: https://twitter.com/adumb_codesA deep dive into the network of Wikipedia and some of...

Large language models require your data in a graph; let me try to explain why. Behind the power of large language models is an architecture known as the… | 37 comments on LinkedIn

Exploring the Potential of AI-Powered Knowledge Graphs with MindGraph ... Have you ever wondered how we can harness the power of AI to create and explore…

🔍 How can we design neural networks that take neural network parameters as input? This fundamental question arises in applications as diverse as generating…

Knowledge Discovery and Generation: Synergizing Knowledge Graphs and Large Language Models 💡 In today’s era of information explosion, the volume of… | 14 comments on LinkedIn

Reflects company’s strong business momentum and rising AI demand for vector and graph databases

Unlocking the Secrets of Scientific Discovery with AI and Knowledge Graphs ... Have you ever wondered how AI could revolutionize the way we conduct scientific… | 17 comments on LinkedIn

Watch this insightful talk on Entity Resolved Knowledge Graphs, and learn how entity resolution can add insight and utility to graph data.

Using your sample questions as the basis for Knowledge Graph creation, simplifying the creation of structured and deterministic knowledge.

RDF data model, its virtues, vices, and applications.

Linked Data in Production: Moving Beyond Ontologies - Download as a PDF or view online for free

When Large Language Models Meet Knowledge Graphs

Graph Neural Prompting with Large Language Models

How to create synthetic datasets to improve graph-based anomaly detection models? Amazon researchers developed a new method for synthesizing training data for…

🌟GraphGPT🌟 (385 stars in GitHub) is accepted by 🌟SIGIR'24🌟 (only 20.1% acceptance rate)! Thank Yuhao Yang, wei wei, and other co-authors for their precious…

Here is my third blog entry of 2024. In this blog, I consider the slow pace of taxonomy and ontology building vs the rapid pace of machine learning models… | 24 comments on LinkedIn

Long-Context models, such as Google Gemini Pro 1.5 or Large World Model, are probably changing the way we think about RAG (retrieval-augmented generation)… | 12 comments on LinkedIn

Why Graphs? Graph databases are the most powerful tools for managing data because of their structure and flexibility. Here are 6 reasons why: 1. They are…

Wow, in Jensen Huang (CEO) his keynote at NVIDIA #GTC24, he calls out three sources of data to integrate with LLMs: 1) vector databases, 2) ERP / CRM and 3)…

I want to thank Jay (JieBing) Yu, PhD for his hard work in creating a Mini-Me (https://lnkd.in/g6TR543j), a virtual assistant built on his fantastic LLM work…

In the realm of personalization, integrating diverse information sources such as consumption signals and content-based representations is becoming increasingly critical to build state-of-the-art solutions. In this regard, two of the biggest trends in research around this subject are Graph Neural Networks (GNNs) and Foundation Models (FMs). While GNNs emerged as a popular solution in industry for powering personalization at scale, FMs have only recently caught attention for their promising performance in personalization tasks like ranking and retrieval. In this paper, we present a graph-based foundation modeling approach tailored to personalization. Central to this approach is a Heterogeneous GNN (HGNN) designed to capture multi-hop content and consumption relationships across a range of recommendable item types. To ensure the generality required from a Foundation Model, we employ a Large Language Model (LLM) text-based featurization of nodes that accommodates all item types, and construct the graph using co-interaction signals, which inherently transcend content specificity. To facilitate practical generalization, we further couple the HGNN with an adaptation mechanism based on a two-tower (2T) architecture, which also operates agnostically to content type. This multi-stage approach ensures high scalability; while the HGNN produces general purpose embeddings, the 2T component models in a continuous space the sheer size of user-item interaction data. Our comprehensive approach has been rigorously tested and proven effective in delivering recommendations across a diverse array of products within a real-world, industrial audio streaming platform.