Over the past 5 years, news about AI has been filled with amazing research – at first focused on graph neural networks (GNNs) and more recently about large language models (LLMs). Understand that business tends to use connected data – networks, graphs – whether you’re untangling supply networks in Manufacturing, working on drug discovery for Pharma, or mitigating fraud in Finance. Starting from supplier agreements, bill of materials, internal process docs, sales contracts, etc., there’s a graph inside nearly every business process, one that is defined by language. This talk addresses how to leverage both natural language and graph technologies together for AI applications in industry. We’ll look at how LLMs get used to build and augment graphs, and conversely how graph data gets used to ground LLMs for generative AI use cases in industry – where a kind of “virtuous cycle” is emerging for feedback loops based on graph data. Our team has been engaged, on the one hand, with enterprise use cases in manufacturing. On the other hand we’ve worked as intermediaries between research teams funded by enterprise and open source projects needed by enterprise – particularly in the open source ecosystem for AI models. Also, there are caveats; this work is not simple. Translating from latest research into production-ready code is especially complex and expensive. Let’s examine caveats which other teams should understand, and look toward practical examples.

In this blog post, we will explore how Knowledge Graphs can benefit from LLMs, and vice versa.

(This post & prompt technique was inspired by a conversation with Nicky Clarke and a recent post by Kurt Cagle, Rethinking Hypergraphs.) Building prompts for ultra-large context windows requires a complete reimagining of how prompts are created.

KGLens: Unlocking Hidden Connections: Knowledge Graphs Reveal What Models Don't Know …. Evaluating AI Knowledge Just Got Smarter … How do we know if AI…

Although I have of course heard this question more often in recent months than in all the years before, it is really just a reiteration of the question of all questions, which is probably the most fundamental question of all for AI: How much human (or symbolic AI) does statistical AI need? With ever

Microsoft is transforming retrieval-augmented generation with GraphRAG, using LLM-generated knowledge graphs to significantly improve Q&A when analyzing complex information and consistently outperforming baseline RAG. Get the details.

Leveraging LLMs for Causal Reasoning: Why Knowledge and Algorithms are Key 🌳 Causal reasoning — the capacity to understand cause-effect relationships and…

Ontologies and Knowledge Graphs offer a way to connect embedding vectors to structured knowledge, enhancing their meaning and explainability. Let's delve into… | 25 comments on LinkedIn

A Step-by-step Walkthrough with GenAI-Stack and OpenAI

We explore the 3 types of recursive retrieval — Page-Based, Information-Centric, and Concept-Centric retrieval

There is no intelligence without knowledge. Despite the incredible power of Large Language Models (LLM), they still significantly struggle…

Ontologies are the backbone of the Semantic Web bridging the gap between human and machine understanding. They define the concepts and relationships that… | 29 comments on LinkedIn

How to Combine RAG, Reinforcement Learning, and Knowledge Graphs for More Robust AI Agents ⭕ Recent months have seen rapid progress in developing large…

With this demo, we wanted to demonstrate 3 key powerful applications of knowledge graph in RAG, and demonstrate how it can improve RAG…

Leveraging Large Language Models to Assemble Knowledge Graphs Grounded in Reality 🔺 The exponential growth of data has brought both vast opportunities and… | 17 comments on LinkedIn

Several scientists in the first half of the 20th century made enormous contributions to science and technology in different fields. We can mention sever...

Entity alignment, which is a prerequisite for creating a more comprehensive Knowledge Graph (KG), involves pinpointing equivalent entities across disparate KGs. Contemporary methods for entity alignment have predominantly utilized knowledge embedding models to procure entity embeddings that encapsulate various similarities-structural, relational, and attributive. These embeddings are then integrated through attention-based information fusion mechanisms. Despite this progress, effectively harnessing multifaceted information remains challenging due to inherent heterogeneity. Moreover, while Large Language Models (LLMs) have exhibited exceptional performance across diverse downstream tasks by implicitly capturing entity semantics, this implicit knowledge has yet to be exploited for entity alignment. In this study, we propose a Large Language Model-enhanced Entity Alignment framework (LLMEA), integrating structural knowledge from KGs with semantic knowledge from LLMs to enhance entity alignment. Specifically, LLMEA identifies candidate alignments for a given entity by considering both embedding similarities between entities across KGs and edit distances to a virtual equivalent entity. It then engages an LLM iteratively, posing multiple multi-choice questions to draw upon the LLM's inference capability. The final prediction of the equivalent entity is derived from the LLM's output. Experiments conducted on three public datasets reveal that LLMEA surpasses leading baseline models. Additional ablation studies underscore the efficacy of our proposed framework.

WhyHow.AI's self-learning RAG with knowledge graphs to bring accuracy and rules to Vertical AI - demonstrating recursive retrieval, memory, automated context-aware knowledge graph construction.

Knowledge Graphs Achieve Superior Reasoning versus Vector Search alone for Retrieval Augmentation 🔗 As artificial intelligence permeates business… | 29 comments on LinkedIn

Neuralizing Retrieval: Infusing Symbolic Reasoning into Language Models through Algorithmic Alignment ↗ Retrieval-augmented generation (RAG) has emerged as…

Leveraging Graphs to Advance Chain-of-Thought Reasoning ⛓ Chain-of-thought (CoT) prompting has rapidly emerged as a technique to substantially improve the…

Knowledge Graphs are, IMHO, the best way to structure data for any subsequent analysis. The problem is that the majority of data that is…

In my previous post,

Reasoning with Knowledge Graph Clustering in Retrieval-Augmented Generation Systems 🔲 ⚫ Retrieval-augmented generation (RAG) systems have gained immense… | 35 comments on LinkedIn

The secret of AI: it’s all about building your Knowledge Graphs. This open secret deserves more spotlight: a LLM, at its core, is fundamentally a database, a… | 69 comments on LinkedIn

Injecting KGs in RAG in pre-processing, post-processing, chunk extraction, document and contextual hierarchies, with a concrete example.

Use Gartner’s impact radar for generative AI to plan investments and strategy with four key themes in mind: ☑️Model-related innovations ☑️Model performance and AI safety ☑️Model build and data-related ☑️AI-enabled applications Explore all 25 technologies and trends: https://www.gartner.com/en/articles/understand-and-exploit-gen-ai-with-gartner-s-new-impact-radar

What do we mean when we say something is a kind of thing? I’ve been wrestling with that question a great deal of late, partly because I think the role of the ontologist transcends the application of knowledge graphs, especially as I’ve watched LLMs and Llamas become a bigger part of the discussion.

Fusion Knowledge Graphs and Language Models Through Compatible Generative Modeling 🌙 Knowledge graphs (KGs) and large language models (LLMs) have…

Knowledge engineering is a discipline that focuses on the creation and maintenance of processes that generate and apply knowledge. Traditionally, knowledge engineering approaches have focused on knowledge expressed in formal languages. The emergence of large language models and their capabilities to effectively work with natural language, in its broadest sense, raises questions about the foundations and practice of knowledge engineering. Here, we outline the potential role of LLMs in knowledge engineering, identifying two central directions: 1) creating hybrid neuro-symbolic knowledge systems; and 2) enabling knowledge engineering in natural language. Additionally, we formulate key open research questions to tackle these directions.