The SEMIC Style Guide for Semantic Engineers provides guidelines for developing and reusing semantic data specifications, particularly eGovernment Core…

Exploring OWL Ontologies Visually: A Paradigm Shift in Understanding 🌐 In the world of semantic web 🌐 and ontology modeling, inverse properties are a… | 24 comments on LinkedIn

What are the key components of an ontology? Ontologies can seem a bit abstract at first, but when you break them down into their core components, they become… | 21 comments on LinkedIn

How is an ontology different from a schema? At first glance, ontologies and schemas might seem similar, they both organize and define data. But the… | 54 comments on LinkedIn

Exploring OWL Ontologies Visually: A Paradigm Shift in Understanding 🌐 When diving into the world of Web Ontologies (OWL), it's easy to get caught up in…

Copyright 2025 Kurt Cagle/The Cagle Report In my last post, I talked about ontologies as language toolkits, but I'm going to take a somewhat different tack with this piece: exploring the relationship between and ontology and a knowledge graph. Ontologies = Schemas + Taxonomies Let me repeat my opera

We contributed recently to the "awesome semantic shapes" repository. This is a community-curated list of RDF shape resources, be it validators, generators…

How can we reduce the ambiguity between knowledge graph and ontology? The confusion arises because knowledge graphs are often Labeled Property Graphs:… | 103 comments on LinkedIn

In the last few weeks, I’ve been diving into the world of #AgenticAI, and I found quite a mess with definitions, which creates a lot of misunderstanding… | 13 comments on LinkedIn

What if creating Linked Open Data was less like coding and more like writing? Could anyone extend the Semantic Web by sharing a document? Publish a knowledge… | 13 comments on LinkedIn

Great presentation by Ora Lassila on "Semantic Web and AI: Can we finally realize the vision?"

In today’s digital scenario, where AI-driven search and discovery platforms are redefining the rules of online engagement, businesses must rethink how they structure and present their data. At Connected Data London 2024, I had the privilege to speak about the transformative power of knowledge graphs

How Semantic Partners converted the SFIA competency framework dataset to RDF, and made it available through the SFIA foundation.

Wonderful debate here. Let’s set the record straight—if you’re still stuck thinking schema markup is just a checkbox for rich results, you’re not only missing the point but also losing the game.

Dear LinkedIn Fam, We need to have a conversation about something… Label Property Graphs (LPGs) are not ontological-based knowledge graphs because they lack…

I'm coming around to the idea of ontologies. My experience with entity extraction with LLMs has been inconsistent at best. Even running the same request with… | 63 comments on LinkedIn

15 November 2024 Bob Kasenchak, Factor As taxonomy consultants at Factor, we are frequently engaged to help our clients clean up and improve existing taxonomies; these have often developed ad hoc and, often, have not been constructed or managed by taxonomists. This is fine! Having controlled lists o

Can Ontologies be seen as General Ledger for AI? Could that be a good way to audit AI systems delivering critical business outcomes? In my quest to develop a… | 69 comments on LinkedIn

The Cross-Domain Interoperability Framework (CDIF) is designed to support FAIR implementation by establishing a ‘lingua franca’, based on existing standards and technologies to support interoperability, in both human- and machine-actionable fashion. CDIF is a set of implementation recommendations, based on profiles of common, domain-neutral metadata standards which are aligned to work together to support core functions required by FAIR. This report presents a core set of five CDIF profiles, which address the most important functions for cross-domain FAIR implementation.  Discovery (discovery of data and metadata resources) Data access (specifically, machine-actionable descriptions of access conditions and permitted use) Controlled vocabularies (good practices for the publication of controlled vocabularies and semantic artefacts) Data integration (description of the structural and semantic aspects of data to make it integration-ready) Universals (the description of ‘universal’ elements, time, geography, and units of measurement). Each of these profiles is supported by specific recommendations, including the set of metadata fields in specific standards to use, and the method of implementation to be employed for machine-level interoperability. A further set of topics is examined, establishing the priorities for further work. These include: Provenance (the description of provenance and processing) Context (the description of ‘context’ in the form of dependencies between fields within the data and a description of the research setting) Perspectives on AI (discussing the impacts of AI and the role that metadata can play) Packaging (the creation of archival and dissemination packages)  Additional Data Formats (support for some of the data formats not fully supported in the initial release, such as NetCDF, Parquet, and HDF5).  In each of these topics, current discussions are documented, and considerations for further work are provided. Visit WorldFAIR online at http://worldfair-project.eu. WorldFAIR is funded by the EC HORIZON-WIDERA-2021-ERA-01-41 Coordination and Support Action under Grant Agreement No. 101058393.

Attention #Data and #Aalytics Leaders. Our team has fielded over 500 inquires on the subject of #DataProducts. In fact it is now one of the most popular topics… | 28 comments on LinkedIn

Data semantics is harder than rocket science! Hear Juan Sequeda argue why enterprises need to be investing in semantics. https://lnkd.in/gKV-Prk4 High…

Hello wonderful people, I’ve noticed some common misconceptions about graphs and AI circulating on LinkedIn, and I thought it might be helpful to share some insights to clarify these topics. I hope…

🧠 Knowledge-driven data access means accessing data through an ontology used to represent its meaning at a conceptual level. 🔎 Connecting an enterprise…

A recent post I wrote on LinkedIn hit a nerve. Talking about information/semantic rich projects failing due to the lack of organizational commitment is not new, for me or others in the field.

As a technology SME in the KM space, I am excited about the changes happening in the semantic software industry. Just two years ago, in my book, I provided a complete analysis of the leading providers of taxonomy and ontology management systems, as well as graph providers, auto-tagging systems, and more. While the software products I evaluated are still around, most of them have new owners.

RDF vs LPG: Friends or Foes? For over a decade, ever since #KnowledgeGraphs (KGs) gained prominence, there has been intense competition between #RDF (also…

Implementing Semantic Data Products: A Comprehensive Blueprint for Success As we conclude our series on semantic data products, let's put all the pieces… | 15 comments on LinkedIn

Knowledge Graph / Concept Model: Same or Different? In my understanding when people say 'knowledge graph' they are usually talking about something OWL/RDF-ish,… | 42 comments on LinkedIn