A Map with No Rules The new AI Risk “ontology” (AIRO) maps regulatory concepts from the EU AI Act, ISO/IEC 23894, and ISO 31000. But without formal constraints or ontological grounding in a top-level ontology, it reads more like a map with no rules. At first glance, AIRO seems well-structured. It defines entities like “AI Provider,” “AI Subject,” and “Capability,” linking them to legal clauses and decision workflows. But it lacks the logical scaffolding that makes semantic models computable. There are no disjointness constraints, no domain or range restrictions, no axioms to enforce identity or prevent contradiction. For example, if “Provider” and “Subject” are just two nodes in a graph, the system has no way to infer that they must be distinct. There’s nothing stopping an implementation from assigning both roles to the same agent. That’s not an edge case. It’s a missing foundation. This is where formal ontologies matter. Logic is not a luxury. It’s what makes it possible to validate, reason, and automate oversight. Without constraints and grounding in a TLO, semantic structures become decorative. They document language, but not the conditions that govern responsible behavior. If we want regulations that adapts with AI instead of chasing it, we need more than a vocabulary. We need logic, constraints, and ontological structure. #AIRegulation #ResponsibleAI #SemanticGovernance #AIAudits #AIAct #Ontologies #LogicMatters

In the world of representing and storing knowledge, things or entities have long monopolized center stage. Rows in databases represent things, where columns are just used to describe them.

A second usecase Thomas wrote for Veronika Heimsbakk’s SHACL for the Practitioner upcoming book is about Sparna’s work for the European Parliament. From validation of the data in the knowledge graph to further projects of data integration and dissemination, many different usages of SHACL specifications were explored… … and more exploratory usages of SHACL are foreseen ! “…

Transform UML into a formal OWL ontology and SHACL shapes - OP-TED/model2owl

Why Labeled Property Graphs Break Reasoning — Even with RDF Interop At first glance, it seems like LPGs can handle reasoning over RDF data if you just install… | 14 comments on LinkedIn

It seems the answer on whether an LMM would be able to replace the whole text-to-ontology pipeline is a resounding ‘no’. If you’re one of those who think that should be (or even is?) a ‘yes’: why, and did you do the experiments that show it’s as good as the alternatives (with the results available)? And I mean a proper ontology, not a knowledge graph with numerous duplications and contradictions and lacking constraints. For a few gentle considerations (and pointers to longer arguments) and a summary figure of processes the LLM supposedly would be replacing: see https://lnkd.in/dG_Xsv_6 | 43 comments on LinkedIn

Unpack what semantics is, walking through the benefits of a semantic approach to your data across search, usability, and standardization.

The Need for a Structured Approach, Elements of the Ontology Pipeline, the Pipeline as a Framework for Developing Knowledge Management Systems, and More!

The amount of biomedical data is growing, and managing it is increasingly challenging. While Findable, Accessible, Interoperable and Reusable (FAIR) data principles provide guidance, their adoption has proven difficult, especially in larger enterprises like pharmaceutical companies. In this manuscript, we describe how we leverage an Ontology-Based Data Management (OBDM) strategy for digital transformation in Novo Nordisk Research & Early Development. Here, we include both our technical blueprint and our approach for organizational change management. We further discuss how such an OBDM ecosystem plays a pivotal role in the organization’s digital aspirations for data federation and discovery fuelled by artificial intelligence. Our aim for this paper is to share the lessons learned in order to foster dialogue with parties navigating similar waters while collectively advancing the efforts in the fields of data management, semantics and data driven drug discovery.

Scientific Data - A semantic approach to mapping the Provenance Ontology to Basic Formal Ontology

💫 An 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗼𝗻𝘁𝗼𝗹𝗼𝗴𝘆 is just a means, not an end. 👉 Transforming 𝘁𝗮𝗰𝗶𝘁 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 into 𝗲𝘅𝗽𝗹𝗶𝗰𝗶𝘁 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 through an enterprise ontology is a self-contained exercise if not framed within a broader process of knowledge creation, collection, and distribution within the organization. 👇 The 𝗦𝗘𝗖𝗜 𝗠𝗼𝗱𝗲𝗹 effectively describes the various steps of this process, going beyond mere collection and formalization. The SECI model outlines the following four phases that must be executed iteratively and continuously to properly manage organizational knowledge: 1️⃣ 𝗦𝗼𝗰𝗶𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: In this phase, tacit knowledge is shared through direct interaction, observation, or experiences. It emphasizes the transfer of personal knowledge between individuals and fosters mutual understanding through collaboration (tacit ➡️ tacit). 2️⃣ 𝗘𝘅𝘁𝗲𝗿𝗻𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: In this phase, tacit knowledge is articulated into explicit forms, such as an enterprise ontology. It helps to codify and communicate the personal knowledge that might otherwise remain unspoken or difficult to share (tacit ➡️ explicit). 3️⃣ 𝗖𝗼𝗺𝗯𝗶𝗻𝗮𝘁𝗶𝗼𝗻: In this phase, explicit knowledge is gathered from different sources, categorized, and synthesized to form new sets of knowledge. It involves the aggregation and reorganization of existing knowledge to create more structured and accessible forms (explicit ➡️ explicit). 4️⃣ 𝗜𝗻𝘁𝗲𝗿𝗻𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: In this phase, individuals internalize explicit knowledge, turning it back into tacit knowledge through practice, experience, and learning. It emphasizes the transformation of formalized knowledge into personal, actionable knowledge (explicit ➡️ tacit). 🎯 In a world where the only constant is change, it is no longer enough for an organization to know something; what matters most is how fast it learns by creating and redistributing new knowledge internally. 🧑‍🎓 To quote Nadella, organizations and the people within them should not be 𝘒𝘯𝘰𝘸-𝘐𝘵-𝘈𝘭𝘭𝘴 but rather 𝘓𝘦𝘢𝘳𝘯-𝘐𝘵-𝘈𝘭𝘭𝘴. #TheDataJoy #KnowledgeMesh #KnowledgeManagement #Ontologies

In this blog, Ben Kass walks through the minimum requirements to call something a "semantic layer," and how the pieces connect to each other.

What makes an ontology fail? 9 reasons. At the inauguration of SCOR (Swiss Center for Ontological Research), I had the opportunity to speak alongside Barry… | 154 comments on LinkedIn

🌟 Calling all teachers, students and practitioners in the Semantic Web and knowledge graph community! 🌟 Looking for a fresh, engaging dataset to build… | 18 comments on LinkedIn

Copyright 2025 Kurt Cagle / The Ontologist

Can work on ontologies with Neo4j, including those based on OWL (Web Ontology Language). However, Neo4j alone is not an ontology reasoner.

Scientific Data - A semantic approach to mapping the Provenance Ontology to Basic Formal Ontology

📚 In recent years, several specifications have emerged to define data assets managed as products. Today, two main types of specifications exist: 1️⃣ 𝗗𝗮𝘁𝗮 𝗖𝗼𝗻𝘁𝗿𝗮𝗰𝘁 𝗦𝗽𝗲𝗰𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 (𝗗𝗖𝗦): Focused on describing the data asset and its associated metadata. 2️⃣ 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗦𝗽𝗲𝗰𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 (𝗗𝗣𝗦): Focused on describing the data product that manages and exposes the data asset. 👉 The 𝗢𝗽𝗲𝗻 𝗗𝗮𝘁𝗮 𝗖𝗼𝗻𝘁𝗿𝗮𝗰𝘁 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱 (𝗢𝗗𝗖𝗦) by Bitol is an example of the first specification type, while the 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗼𝗿 𝗦𝗽𝗲𝗰𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 (𝗗𝗣𝗗𝗦) by the Open Data Mesh Initiative represents the second. 🤔 But what are the key differences between these two approaches? Where do they overlap, and how can they complement each other? More broadly, are they friends, enemies, or frenemies? 🔎 I explored these questions in my latest blog post. The image below might give away some spoilers, but if you're curious about the full reasoning, read the post. ❤️ I'd love to hear your thoughts! #TheDataJoy #DataContracts #DataProducts #DataGovernance | 29 comments on LinkedIn

What are the key ontology standards you should have in mind? Ontology standards are crucial for knowledge representation and reasoning in AI and data… | 32 comments on LinkedIn

In my last post, AI Supported Taxonomy Term Generation, I used an LLM to help generate candidate terms for the revision of a topic taxonomy that had fallen out of sync with the content it was meant to tag. In that example, the taxonomy in question is for the "Insights" articles on my consulting webs

Ontology is not only about data! Many people think that ontologies are only about data (information). But an information model provides only one perspective… | 85 comments on LinkedIn

Enterprise Ontology: A Human-Centric Approach to Understanding the Essence of Organisation : Dietz, Jan L. G., Mulder, Hans B. F.: Amazon.nl: Boeken

Organisations have oceans of data, but most remains siloed, fragmented, and underutilized. Enterprise Knowledge Graphs are a practical and scalable solution…

Nicola Guarino Keynote Address for the Ontology Summit 2025 on 22 January 2025 "Ontologies as specifications of conceptualizations: correctness, precision, a...

Terminology Augmented Generation (TAG)? Recently some fellow terminologists have proposed the new term "Terminology-Augmented Generation (TAG)" to refer to… | 29 comments on LinkedIn

Mapping Workbench transforms XML data into harmonized RDF using Precise Mapping. This is a collaborative tool used by semantic engineers to efficiently map…

A few weeks ago, Thomas Francart asked me what we put in OWL, what we put in SHACL, and what our rule of thumb is to decide. I wrote this post to answer these…

Author: Nicolas Figay Status: DraftAuthor: Nicolas Figay Status: Draft Last update: 2025-01-14 This article was initiated due to the success of the following post A post being not enough for addressing the topic, here is the article developing the subject deeper. Introduction When diving into the wo