Data provenance is something people love in theory, but never practice... I have just rewatched an excellent appearance by Jaron Lanier from a couple of… | 39 comments on LinkedIn

Relational Harmony and a New Hope for Dimensionality ⛓️ In an era where data complexity escalates and the quest for meaningful technology integration… | 30 comments on LinkedIn

Knowledge hypergraphs are garnering a lot of attention – and deservedly so. You can find my two previous posts on knowledge hypergraphs and on more adaptive conceptualizations for hypergraphs as well as Kurt Cagle's focused and more practically minded pieces on Hypergraphs and RDF, on Named Graphs (

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.

Rapid Growth in Data Volume and Complexity

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.

Leveraging Structured Knowledge to Automatically Detect Hallucination in Large Language Models 🔺 🔻 Large Language Models has sparked a revolution in AI’s… | 25 comments on LinkedIn

Sometimes the words “taxonomy” and “ontology” are used interchangeably, and while they are closely related, they are not the same thing. They are both considered kinds of knowledge organization systems to support information and knowledge management.

Semantics friends and colleagues, Announcing the release of a new front-end application development library for RDF. Most of us have heard time and again how…

(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.

OccO: Occupation Ontology. Contribute to Occupation-Ontology/OccO development by creating an account on GitHub.

Copyright 2024. Kurt Cagle When you look at your company, you likely see things - people, clients or customers, products, processes, roles, revenue, etc.

From 5th to 9th of February, I had the privilege to participate to a seminar entitled "Are Knowledge Graphs Ready for the Real World? Challenges and Perspective" at the Dagstuhl Schlosse. It was my third Dagstuhl experience, so I was ready for a very intense week of work and discussion and I have to

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

Concepts form the very core of all our work on representing, accumulating, and manipulating knowledge with algorithms. Without them, we would simply have no framework to even start thinking about knowledge.

Symbolic Artificial Intelligence, Semantic Web State of the art The Semantic Web project was formulated at the end of the 20th century and is based on the availability of inference engines and onto…

Entity-based SEO is the future. Discover the art of crafting search-relevant content through expert entity extraction and linking techniques.

Yes, Structured Data Markup = E-E-A-T fairy dust, or so the popular answer goes. Complete nonsense, because magic does not compute! The real story is...

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.

Semantic Web and Labeled Property Graphs have their use-cases, but complex, real-time business operations is not one of them

A semantic layer for end-to-end, intelligent automation

And this is the amazing prof. Maurizio Lenzerini on the benefits of using ontologies for data integration, captured when showing the long journey of knowledge…

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