Ontology Alignment (OA) is fundamental for achieving semantic interoperability across diverse knowledge systems. We present OntoAligner, a comprehensive, modular, and robust Python toolkit for...

Repository for the Procedural Knowledge Ontology (PKO) - GitHub - perks-project/pk-ontology: Repository for the Procedural Knowledge Ontology (PKO)

Last week, I was happy to be able to attend the 22nd European Semantic Web Conference. I’m a regular at this conference and it’s great to see many friends and colleagues as well as meet…

In a knowledge graph, more expressive nodes are clearly more useful, dramatically more valuable nodes – when we focus on the right nodes. This was a key lesson I learned building knowledge graphs at LinkedIn with the terrific team that I assembled.

Want to Fix LLM Hallucination? Neurosymbolic Alone Won’t Cut It The Conversation’s new piece makes a clear case for neurosymbolic AI—integrating symbolic logic with statistical learning—as the long-term fix for LLM hallucinations. It’s a timely and necessary argument: “No matter how large a language model gets, it can’t escape its fundamental lack of grounding in rules, logic, or real-world structure. Hallucination isn’t a bug, it’s the default.” But what’s crucial—and often glossed over—is that symbolic logic alone isn’t enough. The real leap comes from adding formal ontologies and semantic constraints that make meaning machine-computable. OWL, Shapes Constraint Language (SHACL), and frameworks like BFO, Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE), the Suggested Upper Merged Ontology (SUMO), and the Common Core Ontologies (CCO) don’t just “represent rules”—they define what exists, what can relate, and under what conditions inference is valid. That’s the difference between “decorating” a knowledge graph and engineering one that can detect, explain, and prevent hallucinations in practice. I’d go further: • Most enterprise LLM hallucinations are just semantic errors—mislabeling, misattribution, or class confusion that only formal ontologies can prevent. • Neurosymbolic systems only deliver if their symbolic half is grounded in ontological reality, not just handcrafted rules or taxonomies. The upshot: We need to move beyond mere integration of symbols and neurons. We need semantic scaffolding—ontologies as infrastructure—to ensure AI isn’t just fluent, but actually right. Curious if others are layering formal ontologies (BFO, DOLCE, SUMO) into their AI stacks yet? Or are we still hoping that more compute and prompt engineering will do the trick? #NeuroSymbolicAI #SemanticAI #Ontology #LLMs #AIHallucination #KnowledgeGraphs #AITrust #AIReasoning

In the long tradition of dictionaries, the essence of meaning has always relied on two elements: a symbol (usually a word or a phrase) and a definition—an intelligible explanation composed using other known terms. This recursive practice builds a web of meanings, where each term is explained using o

In complex engineering systems, how can we ensure that design knowledge doesn’t get lost in spreadsheets, silos, or forgotten documents? One of the greatest challenges in design domain and product development isn’t a lack of data, but a lack of meaningful, connected knowledge. This is where ontologies come in. An ontology is more than just a taxonomy or glossary. It’s a formal representation of concepts and relationships that enables shared understanding across teams, tools, and disciplines. In the design domain, ontologies serve as a semantic backbone, helping engineers and systems interpret, reuse, and reason over knowledge that would otherwise remain trapped in silos. Why does this matter? Because design decisions are rarely made in isolation. Whether it’s integrating functional models, analysing field failures, or updating risk assessment documents, we need a way to bridge data across multiple sources and domains. Ontologies enable that integration by creating a common language and structured relationships, allowing information to flow intelligently from design to deployment. Ontology-driven systems also support human decision-making by enhancing traceability, contextualising feedback, and enabling AI-powered insights. It’s not about replacing designers, it’s about augmenting their intuition with structured, reusable knowledge. As we move towards more data-driven and model-based approaches in engineering, ontologies are key to unlocking collaboration, innovation, and resilience in product development. #Ontology #KnowledgeEngineering #SystemsThinking #DesignThinking #SystemEngineering #AI #DigitalEngineering #MBSE #KnowledgeSharing #DecisionSupport #AugmentedIntelligence | 16 comments on LinkedIn

I'm happy to share the draft of the "Semantically Composable Architectures" mini-paper. It is the culmination of approximately four years' work, which began with Coreless Architectures and has now evolved into something much bigger. LLMs are impressive, but a real breakthrough will occur once we surpass the cognitive capabilities of a single human brain. Enabling autonomous large-scale system reverse engineering and large-scale autonomous transformation with minimal to no human involvement, while still making it understandable to humans if they choose to, is a central pillar of making truly groundbreaking changes. We hope the ideas we shared will be beneficial to humanity and advance our civilization further. It is not final and will require some clarification and improvements, but the key concepts are present. Happy to hear your thoughts and feedback. Some of these concepts underpin the design of the Product X system. Part of the core team + external contribution: Andrew Barsukov Andrey Kolodnitsky Sapta Girisa N Keith E. Glendon Gurpreet Sachdeva Saurav Chandra Mike Diachenko Oleh Sinkevych | 13 comments on LinkedIn

The number of published scholarly articles is growing at a significant rate, making scholarly knowledge organization increasingly important. Various approaches have been proposed to organize...

Spacetime and information both have the basics of a geometry

A few challenges We keep redoing what has already been done elsewhere in the company. We don’t learn from our mistakes.

Learn how a semantic layer helps with data silos & AI hallucinations and how to unify data & build reliable AI.

Building on Decades of Foundational Research The formal ontology community has given us incredible foundations - Barry Smith's BFO framework, Alan Ruttenberg's CLIF axiomatizations, and Microsoft Research's Z3 theorem prover. What happens when we combine these mature technologies with modern graph d

“The greatest enemy of progress is the illusion of knowledge.”

On request, this is the complete slide deck I used in my course at the C-FORS summer school on Foundational Ontologies (see https://lnkd.in/e9Af5JZF) at the University of Oslo, Norway. If you want to know more, here are some papers related to the talk: On the ontology itself: a) for a gentle introduction to UFO: https://lnkd.in/egS5FsQ b) to understand the UFO history and ecosystem (including OntoUML): https://lnkd.in/emCaX5pF c) a more formal paper on the axiomatization of UFO but also with examples (in OntoUML): https://lnkd.in/e_bUuTMa d) focusing on UFO's theory of Types and Taxonomic Structures: https://lnkd.in/eGPXHeh e) focusing on its Theory of Relations (including relationship reification): https://lnkd.in/eTFFRBy8 and https://lnkd.in/eMNmi7-B f) focusing on Qualities and Modes (aspect reification): https://lnkd.in/eNXbrKrW and https://lnkd.in/eQtNC9GH g) focusing on events and processes: https://lnkd.in/e3Z8UrCD, https://lnkd.in/ePZEaJh9, https://lnkd.in/eYnirFv6, https://lnkd.in/ev-cb7_e, https://lnkd.in/e_nTwBc7 On the tools: a) Model Auto-repair and Constraint Learning: https://lnkd.in/esuYSU9i b) Model Validation and Anti-Pattern Detection: https://lnkd.in/e2SxvVzS c) Ontological Patterns and Pattern Grammars: https://lnkd.in/exMFMgpT and https://lnkd.in/eCeRtMNz d) Multi-Level Modeling: https://lnkd.in/eVavvURk and https://lnkd.in/e8t3sMdU e) Complexity Management: https://lnkd.in/eq3xWp-U f) FAIR catalog of models and Pattern Mining: https://lnkd.in/eaN5d3QR and https://lnkd.in/ecjhfp8e g) Anti-Patterns on Wikidata: https://lnkd.in/eap37SSU h) Model Transformation/implementation: https://lnkd.in/eh93u5Hg, https://lnkd.in/e9bU_9NC, https://lnkd.in/eQtNC9GH, https://lnkd.in/esGS8ZTb #ontology #UFO #ontologies #foundationalontology #toplevelontology #TLO Semantics, Cybersecurity, and Services (SCS)/University of Twente

1. Introduction: A Tension in Knowledge Representation The classic definition by Thomas Gruber—"an ontology is a formal, explicit specification of a shared conceptualization"—has long served as a foundational reference in knowledge representation.

𝙏𝙝𝙤𝙪𝙜𝙝𝙩 𝙛𝙤𝙧 𝙩𝙝𝙚 𝘿𝙖𝙮: What if we could encapsulate everything a person knows—their entire bubble of knowledge, what I’d call a Personal Knowledge Domain or better, our 𝙎𝙚𝙢𝙖𝙣𝙩𝙞𝙘 𝙎𝙚𝙡𝙛, and represent it in an RDF graph? From that foundation, we could create Personal Agents that act on our behalf. Each of us would own our agent, with the ability to share or lease it for collaboration with other agents. If we could make these agents secure, continuously updatable, and interoperable, what kind of power might we unlock for the human race? Is this idea so far-fetched? It has solid grounding in knowledge representation, identity theory, and agent-based systems. It fits right in with current trends: AI assistants, the semantic web, Web3 identity, and digital twins. Yes, the technical and ethical hurdles are significant, but this could become the backbone of a future architecture for personalized AI and cooperative knowledge ecosystems. Pieces of the puzzle already exist: Tim Berners-Lee’s Solid Project, digital twins for individuals, Personal AI platforms like personal.ai, Retrieval-Augmented Language Model agents (ReALM), and Web3 identity efforts such as SpruceID, architectures such as MCP and inter-agent protocols such as A2A. We see movement in human-centric knowledge graphs like FOAF and SIOC, learning analytics, personal learning environments, and LLM-graph hybrids. What we still need is a unified architecture that: * Employs RDF or similar for semantic richness * Ensures user ownership and true portability * Enables secure agent-to-agent collaboration * Supports continuous updates and trust mechanisms * Integrates with LLMs for natural, contextual reasoning These are certainly not novel notions, for example: * MyPDDL (My Personal Digital Life) and the PDS (Personal Data Store) concept from MIT and the EU’s DECODE project. * The Human-Centric AI Group at Stanford and the Augmented Social Cognition group at PARC have also published research around lifelong personal agents and social memory systems. However, one wonders if anyone is working on combining all of the ingredients into a fully baked cake - after which we can enjoy dessert while our personal agents do our bidding. | 21 comments on LinkedIn

The other day something reminded me of Greenspun's Tenth Rule: "Any sufficiently complicated C or Fortran program contains an ad hoc, informally-specified, bug-ridden, slow implementation of half of Common Lisp." https://en.

We talk about knowledge management and systems for knowledge (like knowledge graphs) a lot these days. Especially with the rising interest in #semantics, #metadata, #taxonomies and #ontologies, thanks to AI. But what makes for knowledge that is operational and actionable? Less often discussed is knowledge infrastructure. Fundamental to knowledge management and knowledge repositories, as derived from the field of library and information science, is a service—oriented approach. Knowledge infrastructure is focused on creating systems that deliver information and knowledge that is accurate and satisfies the requirements of: ⚪️ Creators: those who generate knowledge (researchers, experts, content authors, data producers) ⚪️ Products: the formal outputs of knowledge (e.g., documents, datasets, models, applications, platforms, chatbots/AI assistants) ⚪️ Distributors: systems and platforms that make knowledge available (repositories, databases, APIs) ⚪️ Disseminators: communicators and interpreters (educators, marketers, dashboards, wikis) ⚪️ Users: individuals or systems that apply the knowledge (decisionmakers, AI agents, learners, stakeholders) Let’s put this into perspective. Without supporting knowledge infrastructures, knowledge becomes a one-off, relegated to silos or single use instances. We see this with products. When we manage knowledge as a product, we fail to cast a wider net, assuming successes based on metrics that are localized to the product rather than distributed to be inclusive of all signals, input and output. If knowledge is not managed as infrastructure, we create anti-patterns for the business and AI systems. A recognizable symptom of these anti-patterns are silos. I’ll be publishing an article soon about knowledge infrastructure, and what it takes to build and manage a knowledge infrastructure program. #ai #ia #knowledgeinfrastructure For reference, excerpt from Richard E Rubin’s MLS textbook, Foundations of Information and Library Science in comments 👇👇👇 | 42 comments on LinkedIn

The new AI powered Anayltics stack is here…says Gartner’s Afraz Jaffri ! A key element of that stack is an ontology powered Semantic Layer that serves as the brain for AI agents to act on knowledge of your internal data and deliver timely, accurate and hallucination-free insights! #semanticlayer #knowledgegraphs #genai #decisionintelligence

Last week I was fortunate to attend the Knowledge Graph Conference in NYC! Here are a few trends that span multiple presentations and conversations. - AI and LLM Integration: A major focus [again this year] was how LLMs can be used to enrich knowledge graphs and how knowledge graphs, in turn, can improve LLM outputs. This included using LLMs for entity extraction, verification, inference, and query generation. Many presentations demonstrated how grounding LLMs in knowledge graphs leads to more accurate, contextual, and explainable AI responses. - Semantic Layers and Enterprise Knowledge: There was a strong emphasis on building semantic layers that act as gateways to structured, connected enterprise data. These layers facilitate data integration, governance, and more intelligent AI agents. Decentralized semantic data products (DPROD) were discussed as a framework for internal enterprise data ecosystems. - From Data to Knowledge: Many speakers highlighted that AI is just the “tip of the iceberg” and the true power lies in the data beneath. Converting raw data into structured, connected knowledge was seen as crucial. The hidden costs of ignoring semantics were also discussed, emphasizing the need for consistent data preparation, cleansing, and governance. - Ontology Management and Change: Managing changes and governance in ontologies was a recurring theme. Strategies such as modularization, version control, and semantic testing were recommended. The concept of “SemOps” (Semantic Operations) was discussed, paralleling DevOps for software development. - Practical Tools and Demos: The conference included numerous demos of tools and platforms for building, querying, and visualizing knowledge graphs. These ranged from embedded databases like KuzuDB and RDFox to conversational AI interfaces for KGs, such as those from Metaphacts and Stardog. I especially enjoyed catching up with the Semantic Arts team (Mark Wallace, Dave McComb and Steve Case), talking Gist Ontology and SemOps. I also appreciated the detailed Neptune Q&A I had with Brian O'Keefe, the vision of Ora Lassila and then a chance meeting Adrian Gschwend for the first time, where we connected on LinkML and Elmo as a means to help with bidirectional dataflows. I was so excited by these conversations that I planned to have two team members join me in June at the Data Centric Architecture Workshop Forum, https://www.dcaforum.com/

In a previous post I was touching on how ontologies are foundational to many data activities, yet "obscure". As a consequence, the different roles of ontologies are not always known among people that make use of them, as they may focus only on some of the aspects relevant for specific use cases.

Ontologies, OWL, SHACL: I was going to react to a comment that came through my feed and turned it into this post as it resonated with questions I am often asked about the mentioned technologies, their uses, and their relations and, more broadly, it concerns a key architectural discussion that I've h

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 ! “…