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

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…

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

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

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

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…

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…

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.

Overview

Grab some Oreos and let's chow down some #ontology basics. As an English Literature major, we did talk about quite a few philosophical concepts, but the… | 25 comments on LinkedIn

Recently I watched Andrew Ng and Andreas Kollegger course “Knowledge Graphs for RAG” available at deeplearning.ai. The course builds…

InBox: Enhancing Recommender Systems with Knowledge Graph-based Interest Box Embeddings ... 🧠 (Alibaba Group / Ant Group) Imagine if AI could understand not…

I keep checking in on the Solid project. But I’m baffled by its lack of functionality. I’ve written up some of my questions.

The task of predicting multiple links within knowledge graphs (KGs) stands as a challenge in the field of knowledge graph analysis, a challenge increasingly resolvable due to advancements in natural language processing (NLP) and KG embedding techniques. This paper introduces a novel methodology, the Knowledge Graph Large Language Model Framework (KG-LLM), which leverages pivotal NLP paradigms, including chain-of-thought (CoT) prompting and in-context learning (ICL), to enhance multi-hop link prediction in KGs. By converting the KG to a CoT prompt, our framework is designed to discern and learn the latent representations of entities and their interrelations. To show the efficacy of the KG-LLM Framework, we fine-tune three leading Large Language Models (LLMs) within this framework, employing both non-ICL and ICL tasks for a comprehensive evaluation. Further, we explore the framework's potential to provide LLMs with zero-shot capabilities for handling previously unseen prompts. Our experimental findings discover that integrating ICL and CoT not only augments the performance of our approach but also significantly boosts the models' generalization capacity, thereby ensuring more precise predictions in unfamiliar scenarios.

Bob Kasenchak, Factor One of the regular tasks we undertake when starting an engagement with a new client involves cataloging and evaluating existing taxonomies in their business information ecosystem. But not all taxonomies are created equal; or, perhaps more specifically, not all taxonomies serve