Building Knowledge Graphs in RAG is not just about using graphs as data stores, but also as a means for structured reasoning.

I am thrilled to introduce a new AI Study Guide (https://lnkd.in/g4rPZVHW) dedicated to Tony Seale, another of my favorite authors, thought leaders, and…

Using vector databases in retrieval-augmented generation (RAG) raises concerns about relevance, since the info retrieved may not be semantically related to the…

KGLM-Loop: A Bi-Directional Data Flywheel for Knowledge Graph Refinement and Hallucination Detection in Large Language Models ☀ 🌑 In the pursuit of…

Telecom GenAI based Network Operations: The Integration of LLMs, GraphRAG, Reinforcement Learning, and Scoring Models With the increasing complexity of… | 12 comments on LinkedIn

Writer CEO May Habib says its semantic graphing approach is an alternative to the chunking process of RAG using vector databases.

Achieving Robust Reasoning by Combining Neural and Symbolic Representations 🌗 Recent breakthroughs in large language models (LLMs) demonstrate impressive…

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.

Check out this chatbot (https://lnkd.in/gv8Afk57) that I created entirely based on the prolific writings of Mike Dillinger, PhD . This chatbot helps you better…

"It is possible to build KGs with LMs through prompt engineering. But are we boiling the ocean? Can we improve the quality of the generated graph elements by using - dare I say it - SLMs (small language models)"

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.