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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.

Knowledge graphs have revolutionized the way we organize and analyze data. By visually depicting entities and their interconnections, they…

Welcome to the world of Semantic Web! 🌐 It’s a broad term where ideas and technologies come together to tackle the overwhelming amount of…

Large language models (LLMs) have achieved great success in many fields, and recent works have studied exploring LLMs for graph discriminative tasks such as node classification. However, the abilities of LLMs for graph generation remain unexplored in the literature. Graph generation requires the LLM to generate graphs with given properties, which has valuable real-world applications such as drug discovery, while tends to be more challenging. In this paper, we propose LLM4GraphGen to explore the ability of LLMs for graph generation with systematical task designs and extensive experiments. Specifically, we propose several tasks tailored with comprehensive experiments to address key questions regarding LLMs' understanding of different graph structure rules, their ability to capture structural type distributions, and their utilization of domain knowledge for property-based graph generation. Our evaluations demonstrate that LLMs, particularly GPT-4, exhibit preliminary abilities in graph generation tasks, including rule-based and distribution-based generation. We also observe that popular prompting methods, such as few-shot and chain-of-thought prompting, do not consistently enhance performance. Besides, LLMs show potential in generating molecules with specific properties. These findings may serve as foundations for designing good LLMs based models for graph generation and provide valuable insights and further research.

Social networks crawling is in the focus of active research the last years. One of the challenging task is to collect target nodes in an initially unknown graph given a budget of crawling steps. Predicting a node property based on its partially known neighbourhood is at the heart of a successful crawler. In this paper we adopt graph neural networks for this purpose and show they are competitive to traditional classifiers and are better for individual cases. Additionally we suggest a training sample boosting technique, which helps to diversify the training set at early stages of crawling and thus improves the predictor quality. The experimental study on three types of target set topology indicates GNN based approach has a potential in crawling task, especially in the case of distributed target nodes.

One of the hot topics in machine learning is the field of GNN. The complexity of graph data has imposed significant challenges on existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. This paper represents a survey, providing a comprehensive overview of Graph Neural Networks (GNNs). We discuss the applications of graph neural networks across various domains. Finally, we present an advanced field in GNNs: graph generation.

Navigating the EU AI Act: Introducing the AI Act Requirements Graph As the EU AI Act moves closer to becoming law, understanding its complex web of… | 11 comments on LinkedIn

In this blog post, we’ll be focusing on how to write the retrieval query that supplements or grounds the LLM’s answer.

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