There is an old saying that Democracy is the worst form of government known to mankind, except for all others. The same can be said for the Resource Description Framework or RDF.

Abstraction is an important concept in software development, but to fully embrace the powers of AI, we now need to introduce abstraction into our data… | 74 comments on LinkedIn

As LLMs turn turn data processing into semantic processing, we believe ETL processes will turn into ECL processes.

The Limitations of Cosine Similarity and the Case for Knowledge Graphs in AI ◼ As artificial intelligence (AI) systems become increasingly integrated into…

Today is a pretty special day. In some sense, this is the day I’ve been waiting for all my life. The day that we figure out how to make…

An overview of knowledge graphs and their main use cases

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

A word of caution from Netflix against blindly using cosine similarity as a measure of semantic similarity: https://lnkd.in/gX3tR4YK They study linear matrix… | 12 comments on LinkedIn

Introducing PyGraft: A Powerful Tool for Generating Synthetic Knowledge Graphs ... A new open-source tool called PyGraft is now available that enables the… | 13 comments on LinkedIn

Here are the slides I used for my 09-FEB-2024 keynote presentation "Knowledge Graphs: An Industry Perspective" at Dagstuhl Seminar 24061: "Are Knowledge Graphs…

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…

Knowledge graphs (KGs) have emerged as a prominent data representation and management paradigm. Being usually underpinned by a schema (e.g., an ontology), KGs capture not only factual information but also contextual knowledge. In some tasks, a few KGs established themselves as standard benchmarks. However, recent works outline that relying on a limited collection of datasets is not sufficient to assess the generalization capability of an approach. In some data-sensitive fields such as education or medicine, access to public datasets is even more limited. To remedy the aforementioned issues, we release PyGraft, a Python-based tool that generates highly customized, domain-agnostic schemas and KGs. The synthesized schemas encompass various RDFS and OWL constructs, while the synthesized KGs emulate the characteristics and scale of real-world KGs. Logical consistency of the generated resources is ultimately ensured by running a description logic (DL) reasoner. By providing a way of generating both a schema and KG in a single pipeline, PyGraft's aim is to empower the generation of a more diverse array of KGs for benchmarking novel approaches in areas such as graph-based machine learning (ML), or more generally KG processing. In graph-based ML in particular, this should foster a more holistic evaluation of model performance and generalization capability, thereby going beyond the limited collection of available benchmarks. PyGraft is available at: https://github.com/nicolas-hbt/pygraft.

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…

We've been hearing the term "Semantic layer" without truly understanding the semantics of it. So, here is episode 11 of #DnABytes and today's topic is:…

Situation I stumbled upon an interesting LinkedIn post, courtesy of Kurt Cagle, about the term ‘Data Product,’ authored by Juha Korpela. Naturally, I appreciate the essence of the post, which emphasizes the importance of defining terminology in a platform-agnostic manner.

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

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… | 52 comments on LinkedIn

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

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.