Samuel Holcman: Thank you for tuning in to Real Talk. Be sure to join your host Sam Holtzman again for another edition of our program. We'll have more Real Topics of discussion then. How do you sort out the so-called jargon from real-world practices that work? Do the members of your organization find some business or technology advice utterly confusing? Welcome to Real Talk with Sam Holsman. In this program, we set the record straight and in terms that business people and technology people can understand. Now, here is your host, Sam Holsman. Welcome to this edition of Real Talk. I'm Sam Holtzman, and the topic for this particular broadcast is ontology and AI, the hidden skill that makes architecture and your career work. Why ontologies matter now? Well, what's an ontology? We'll get to that in just a moment. Let's give an example first. Picture a film studio where one director writes DP, another cinematographer, a third logs Director of Photography. And every system still understands these all mean the same role, connects them to camera setups and production schedules and credits, and avoids payroll confusion or duplicate jobs. That seamless shared understanding is ontology in action. In both EACOE Enterprise Architecture and Business Architecture under BACOE, Ontology is the backbone. It tells us what kinds of things exist in the enterprise, how they relate, and how those meanings stay consistent as we automate, integrate, and apply AI. Today, that makes Ontology not just a theoretical idea, but one of the most valuable underused skills in the AI job market and a critical success factor for serious enterprise architecture and serious business architecture. Now, what an ontology really is, it's simply described, as we like to say in our workshops, kinds of things together. Kinds of things together. It's actually the backbone on how we, as human beings, think about almost everything. And then once we understand that, it's the relationships between those things. In information and computer science, an ontology is a formal description of knowledge in a domain, the kinds of things and the relationship between them. It is more than a glossary, it's a structured model of meaning that both humans and machines can use. Ontologies answer two big questions in any domain. Studio, school, city, orchestra. What kinds of things are there? These are your concepts or classes. the categories of things that matter. In a film production studio, example, director, producer, cinematographer, actor, script, scene, schedule, camera, location, equipment, shot, budget, department, vendor, contract, each is a kind of thing the enterprise cares about. The ontology gathers these things, kinds of things, into an organized, shared set that defines what exists in the world and how we distinguish one kind from another. And then we ask, how are those things related? These are the relationships and properties that connect the classes and instances. A director oversees a scene. An actor performs in a scene. A scene uses a camera. A camera requires equipment. Equipment belongs to a department. A department consumes budget. In an ontology, these relationships are explicit, named, and directional and defined. They express how one kind of thing depends on, constraints, or connects to another. An ontology typically contains classes. Director, course, instrument, building. Individuals. A specific instructor, a particular violin, one city park. Attributes, skill level, construction date, duration, capacity. Relationships, teaches course, composes for, located in, manages. Rules and constraints, a student must enroll in at least one course. A music piece must have a composer. A building cannot open if safety certificate equals expired. This is what makes ontology different from simple database schemas. A database stores data. An ontology captures meaning and logic so AI systems and humans can reason over it. Large language models just do not have this. specific understanding. let's take you back in history a little bit. The Dewey Decimal Classification System, a classic ontology example. If all of this sounds abstract, once again think about one of the most widely used knowledge structures in the world, the Dewey Decimal System classification in libraries. Dewey organized all recorded knowledge in ten broad classes. If you remember back 000 to 900, for example, 300 for social sciences, 400 for languages, 500 for science, 600 for technology, 700 for Arts and Recreation. Each main class is then organized into divisions and sections, just using a simple decimal notation, giving you a finer, once again the same phrase, kinds of things together. 600, Technology. 610, Medicine and Health. 616, Diseases. 616.1. diseases of circulatory system, 616.12 heart diseases, or 500 natural sciences, 510 mathematics, 516 geometry, 516.3 analytic geometry, what's happening here? Defines kinds of things together, kinds of things together. Science vs. Technology vs. Arts vs. Literature. Then more specific areas inside each. It defines ontological relationships. Longer numbers represent more specific subjects. Shorter numbers represent broader ones. 616.12 Heart diseases is a more specific kind of 616 diseases, which is more specific kind of 610. Medicine and Health, which sits underneath 600 Technology. Kinds of things together. Dewey behaves like an ontology. We explicitly define what kinds of things there are across all knowledge and how they relate so that people and catalog systems can reliably find and connect information. In enterprise and AI work, your ontology plays the same role Dewey plays in a library. That means you have to have an ontology that is your Dewey Decimal System, your best practices. Dewey tells you where every book belongs and how topics relate. Your ontology tells you where every concept process and data element belongs and how they relate. So data processes and decisions can be discovered, combined, and automated. Now, let's take a look at another example, a formal ontology. like example, the periodic table of music genres. If all of this feels a little too abstract still, think of one of the most intuitive classification systems in the arts, the way streaming services organize music. A little bit more contemporary. Music platforms categorize sound in genres and subgenres. Jazz, smooth fusion bebop. Rock. Alternative, progressive, metal. Classical, Baroque, symphonic, contemporary. Each level refines the last, defining broader and narrower categories through explicit relationships. Well, what's happening here? Defines kinds of things together. Once again, that phrase that you'll hear consistently, kinds of things together. Rock versus jag, jag. Rock vs. Jazz vs. Classical. Then more specific types inside each. It defines ontological relationships. Hard bop is a kind of jazz. Jazz is a kind of music. so on. Dewey does this for books. Genre taxonomies do this for music. Both illustrates ontology's key principle. We define what kinds of things exist and how they relate so humans and systems can reliably connect in search for information. In enterprise and AI work, your ontology plays the same role these systems plays in libraries and music platforms. It is the consistent, shared structure that organizes all the books and tracks of your enterprise data, process, and logic. Why has ontology has been the key to EACoE Enterprise architecture and BACoE Business architecture? Well, at EACoE and BACoE, we're not just drawing boxes and lines. We're building a structured understanding of the enterprise that can be used to make decisions, automate work, and align change with strategy. Ontology is the mechanism that makes this understanding precise, reusable, and computable. as the foundation of what the enterprise is. EACOE's Enterprise Framework organizes architecture into coherent structures, describing future, current, and gap states. Underneath that, you need clarity about what kinds of things, once again that same phrase, what kinds of things exist in the enterprise. Students, classes, campuses, systems, regulations. How they relate. Students attend class. Class belongs to department. Department operates on campus. Policy influences curriculum. That is why ontologies provide a common vocabulary and web of relationships. It becomes your enterprise map, defining conceptual neighborhoods just as a city plan defines streets and districts, or as Dewey defines, shelves and subject areas in the library. In business architecture, the Business Architecture Center of Excellence, BACoE, ontologies are the backbone of business meaning. BACoE, business architecture, emphasize capabilities, information concepts, stakeholders, and policies. Ontologies turn these loose lists into a coherent, semantic model, a model that has meaning. Curriculum uses course material. Accreditation policy governs program design. Faculty performs teaching activity. The ontology-based views lets you connect educational capabilities to systems and AI tools. Trace decisions across roles, concepts, and rules. Keeps a stable organizational blueprint even as the technology shifts underneath. In both the EACoE and BACoE frameworks, ontology is the semantic spine, the spine of meaning, defining kinds of things in the relationships, dependent and independent of any single system or platform, much like the Dewey Decimal System keeps the structure of knowledge stable even as libraries, catalogs, and formats evolve. Now, why is ontology and architecture and AI Exploding now. AI is great for making text, images, and code, but it lacks a structured understanding of specific domains. Ontology fills that gap with a precise map of what exists and how it fits together. Making AI actually understand your domain, large language models know about students or cities, but not in your context. Not in your specific curricula, zoning code, or composition rules. Ontologies encode that domain meaning so AI can specialize. That is why EAI, Enterprise Augmented Information, not AI, but EAI, is the key to removing hallucinations, better known and stated actually as fabrications. In higher education, instance, ontologists define relationships between courses, outcomes, and materials, so educational AI interprets information. The way educators do, this solves the integration nightmare. Real institutions and agencies use many systems, student information, facilities management, payroll, learning management, accreditation records. Each label similar concepts differently. Ontology's let these aligned around shared meaning, much like a music playlist that still links lofty jazz and chill pop and instrumental beats. under related categories, or a library that shelves very different looking books, looking books together under the same Dewey number. Now, how do we make AI explainable and trustworthy? Regulators and boards need to know why a recommendation was made. Why? Ontology-based AI can trace logic. Scholarship denied because GPA is less than 3.0. Credits completed is less than the required threshold. And enrollment status equals part-time. This explanation stems from ontology-defined rules. Just as a Dewey assignment can be traced back to how a subject is classified, this supercharges modern AI applications. In education or creative industries, ontologies group related materials, courses to skills, artists to albums, follow relationships and clarify ambiguous terms. Score is a different meaning in music and not a test grade. Ontologies make AI answers more relevant and consistent. Now, real world ontology in action. In education, an only source of learning truth The university of ontology defines students, courses, learning outcomes, and credentials. Relationships like student completes course, course awards credentials, makes transcript generation, credit transfers, and curriculum design possible across systems. In music production, coordinated creative flow workflows, recording studio use that ontology linking song, instrument, performer, equipment, session, and producer. Relationships like song uses instrument, performer participates in session, session occurs at studio, lets AI automatically tag files, schedule sessions and handle royalties. In urban planning, smarter city operations, a city manager ontology connects buildings, streets, zoning area, utility lines, permits and maintenance events. It identifies that the building lies in zoning area. A zoning area restricts building height and maintenance event impacts utility line. This allows AA to coordinate projects to avoid conflicts. In film production, intelligent post-production workflows. An ontology defines scene, shot, actor, prompt. Editor, schedule, relationships such as scene includes an actor, a shot uses a prop. A scene is edited by an editor. Let's video AI platforms index footage semantically, not just by cryptic file names. Let's take a look at the job market. Why ontology skills pay? The EACoE and BACoE differences. While people are chasing AI engineers and data science titles, enterprise Ontology expertise sits at the high value niche. Studios, schools, governments, and cultural organizations now hire ontology, EACoE, and BACoE architects to make their data and AI usable without hallucinations. Ontology demands logical thinking, systems design, and domain fluency. Those skills are rare, prized, and durable. How do you build your ontology as a career superpower? You can start small and build a visible portfolio, create a mini ontology for something familiar, a podcast network, course catalog, or local music festival. Align it with your current path. Enterprise architects or business architects can use this to explicitly model your domain concepts and relationships. Use them to anchor strategies, systems, and AI coherence across every platform. EACoE and BACoE workshops and certifications have been providing this approach for decades, well before the concept of AI became popular. The future of AI is semantic architecture, architecture that has meaning, not just structure. Throwing more models at a problem does not create understanding. Enterprises need structured, meaningful knowledge AI that can be trusted and explained. Ontologies provide that structure. In EACoE and BoEC practice, ontology bridges business intent architectural logic and AI execution that keeps the enterprise's kinds of things and the relationships stable, much like how music taxonomies or city maps preserve coherent thought while there's constant change and how the Dewey Decimal System classification keeps the world's books consistently organized. And for your career, while others chase the latest neural trend, you can master the semantic infrastructure that makes AI architecture and the integration actually work. Ontologies are not optional anymore. It is the skill quietly shaping the future. Please reach out to us at www.eacoe.org and www.bacoae.org for an exploratory discussion. Bring your career to EA 5.0 and BA 5.0. Others are on this path today.