Knowledge Graph Insights

Dave McComb

During the course of his 25-year consulting career, Dave McComb has discovered both a foundational problem in enterprise architectures and the solution to it.

The problem lies in application-focused software engineering that results in an inefficient explosion of redundant solutions that draw on overlapping data sources.

The solution that Dave has introduced is a data-centric architecture approach that treats data like the precious business asset that it is.

We talked about:

his work as the CEO of Semantic Arts, a prominent semantic technology and knowledge graph consultancy based in the US

the application-centric quagmire that most modern enterprises find themselves trapped in

data centricity, the antidote to application centricity

his early work in semantic modeling

how the discovery of the "core model" in an enterprise facilitates modeling and building data-centric enterprise systems

the importance of "baby step" approaches and working with actual customer data in enterprise data projects

how building to "enduring business themes" rather than to the needs of individual applications creates a more solid foundation for enterprise architectures

his current interest in developing a semantic model for the accounting field, drawing on his history in the field and on Semantic Arts' gist upper ontology

the importance of the concept of a "commitment" in an accounting model

how his approach to financial modeling permits near-real-time reporting

his Data-Centric Architecture Forum, a practitioner-focused event held each June in Ft. Collins, Colorado

Dave's bio

Dave McComb is the CEO of Semantic Arts. In 2000 he co-founded Semantic Arts with the aim of bringing semantic technology to Enterprises. From 2000- 2010 Semantic Arts focused on ways to improve enterprise architecture through ontology modeling and design. Around 2010 Semantic Arts began helping clients more directly with implementation, which led to the use of Knowledge Graphs in Enterprises. Semantic Arts has conducted over 100 successful projects with a number of well know firms including Morgan Stanley, Electronic Arts, Amgen, Standard & Poors, Schneider-Electric, MD Anderson, the International Monetary Fund, Procter & Gamble, Goldman Sachs as well as a number of government agencies. Dave is the author of Semantics in Business Systems (2003), which made the case for using Semantics to improve the design of information systems, Software Wasteland (2018) which points out how application-centric thinking has led to the deplorable state of enterprise systems and The Data-Centric Revolution (2019) which outlines a alternative to the application-centric quagmire.

Prior to founding Semantic Arts he was VP of Engineering for Velocity Healthcare, a dot com startup that pioneered the model driven approach to software development. He was granted three patents on the architecture developed at Velocity. Prior to that he was with a small consulting firm: First Principles Consulting. Prior to that he was part of the problem.

Connect with Dave online

LinkedIn

email: mccomb at semanticarts dot com

Semantic Arts

Resources mentioned in this interview

Dave's books:

The Data-Centric Revolution: Restoring Sanity to Enterprise Information Systems

Software Wasteland: How the Application-Centric Quagmire is Hobbling Our Enterprises

Semantics in Business Systems: The Savvy Manager's Guide

gist ontology

Data-Centric Architecture Forum

Video

Here’s the video version of our conversation:

https://youtu.be/X_hZG7cFOCE

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 29. Every modern enterprise wrestles with its data, trying to get the most out of it. The smartest businesses have figured out that it isn't just "the new oil" - data is the very bedrock of their enterprise architecture. For the past 25 years, Dave McComb has helped companies understand their data, discovering along the way the importance of adopting a data-centric mindset that reveals the essential nature and the true value of this precious asset.

Interview transcript

Larry:

Hi, everyone. Welcome to episode number 29 of the Knowledge Graph Insights Podcast. I am really happy today to welcome to the show Dave McComb. Dave, I think it's safe to say he's a legend in the ontology and knowledge graph worlds. He's the author of three books. One early book called Semantics in Business: The Savvy Manager's Guide, which was probably ahead of its time, which is fine. Dave's that kind of guy. He also wrote the books Software Wasteland and The Data-Centric Revolution, which set the problem that we have in current enterprise architectures and the proposed a solution. Those, by the way, are both under revision. By the end of 2025 or so, we should see new editions of those.

Welcome, Dave. Tell the folks a little bit more about what you're up to these days.

Dave:

Great. Thanks, Larry. Well, we're still running a company here. We have Semantic Arts, probably it's about 30 employees. 20 ontologists and five semantic developers doing God's work, making companies more data-centric. That's what we do now. We go into companies, mostly medium to large-sized companies, and help them.

Dave:

What we've done since the publishing of the book, we started doing it around the publishing of the book, is just figuring out a methodological, and fairly safe and incremental way to get there. Because I think a lot of companies are burned out from so-called legacy modernization projects and digital transformation projects that didn't go well. There's a lot of scar tissue there. We've figured out a way to, first, move some of your data, get it into the graph, get you used to it. Then start moving more, and then more functionality, and just gradually get people there.

Larry:

That's the classic smart consultant, baby steps, proofs of concept.

Dave:

Yeah.

Larry:

Small work out there.

Dave:

Yeah.

Larry:

Hey, let's back up a little bit and talk about, because I'm going to guess that many if not most of my listeners are familiar with you. But for those who aren't, can you talk a little bit about the philosophy? Because you've got a well-articulated philosophy set out in two books about the problem this application-centric quagmire that enterprises got themselves into, and then the data-centric way. Can you talk a little bit about the ... I'd love to know where the idea occurred to you, how you identified the problem, and then a little bit about the two books.

Dave:

Yeah. I started my career with Arthur Anderson, the accounting firm, but they had a consulting division which originally was just called the Administrative Services Division. How innocuous. We worked with the accountants a lot. Then it, as we know, eventually grew into the consulting division, which was Arthur Anderson Consulting, which is now Accenture. They grew like crazy. But back in those early days, we built and implemented mostly accounting systems. I had a career of going around the world, implementing, often building from scratch because it was early days, accounting systems. Including two pretty major full-function ERP systems built from the ground up, and one of them in multi-currency. Pretty sophisticated.

Dave:

It's two things I thought I knew at the time. One, I thought I knew accounting and accounting systems. And I thought I knew the right path for building enterprise applications. But then, right as I was leaving and then as I was doing some independent work on the side, I started to see what was actually going on. That companies were just implementing system, after system, after system. You'd go into a client and they'd have a dozen inventory control systems. You'd go, "Wow, not only do you have a dozen of them." By the way, I'm going to update that and I'll give you some metrics about how many systems most of our clients currently have.

Dave:

What bothered me more was they're all completely arbitrarily different. Not only did every single one of them, which had hundreds or thousands of tables, and each table had dozens of columns, and every one of them had some totally made up name. Some of them, German acronyms, all kinds of stuff. They were even structured differently. You'd go, "Wow. What would cause several different smart people to design an inventory control system and have them come out that different?" We studied database design, third normal form, and all that. If you'd laid out the problem exactly the same, you'd do third normal form, and you'd get to the same answer, but they were not starting from the same place. Then you go, "Wow. Why not? What's going on here?" This is the early '90s.

Dave:

This is back before the World Wide Web, you would have to go to the library to do research. And so we'd go to library, and find magazine articles, and photocopy them, and all those. I know I still have a three-ring binder. There were four articles at that time about applying semantics to information systems. We had devoured, I think, everything that was known at the time. Now, of course, if we did a Google search, there probably was other stuff that we didn't find. We invented this thing we called semantic modeling and said maybe, if you started from what things really mean, you'd actually figure out that inventory actually really means widgets and bins, whatever it is. But you'd hopefully start from the same place and end in the same place.

Dave:

Yeah, that was my observation and how we got into this. A few minutes ago, I'd promised I'd come up with some metric. As we've been going from client to client now, and this is not an exact metric but it's close enough to be scary, take the number of employees you have in your company and divide it by 10, that's probably about how many applications you're currently managing.

Larry:

Wow.

Dave:

Think about it....

Ole Olesen-Bagneux

In every enterprise, says Ole Olesen-Bagneux, the information you need to understand your organization's metadata is already there. It just needs to be discovered and documented.

Ole's Meta Grid can be as simple as a shared, curated collection of documents, diagrams, and data but might also be expressed as a knowledge graph.

Ole appreciates "North Star" architectures like microservices and the Data Mesh but presents the Meta Grid as a simpler way to manage enterprise metadata.

We talked about:

his work as Chief Evangelist at Actian

his forthcoming book, "Fundamentals of Metadata Management"

how he defines his Meta Grid: an integration architecture that connects metadata across metadata repositories

his definition of metadata and its key characteristic, that it's always in two places at once

how the Meta Grid compares with microservices architectures and organizing concepts like Data Mesh

the nature of the Meta Grid as a small, simple, and slow architecture which is not technically difficult to achieve

his assertion that you can't build a Meta Grid because it already exists in every organization

the elements of the Meta Grid: documents, diagrams or pictures, and examples of data

how knowledge graphs fit into the Meta Grid

his appreciation for "North Star" architectures like Data Mesh but also how he sees the Meta Grid as a more pragmatic approach to enterprise metadata management

the evolution of his new book from a knowledge graph book to

his elaboration on the "slow" nature of the Meta Grid, in particular how its metadata focus contrasts with faster real-time systems like ERPs

the shape of the team topology that makes Meta Grid work

Ole's bio

Ole Olesen-Bagneux is a globally recognized thought leader in metadata management and enterprise data architecture. As VP, Chief Evangelist at Actian, he drives industry awareness and adoption of modern approaches to data intelligence, drawing on his extensive expertise in data management, metadata, data catalogs, and decentralized architectures. An accomplished author, Ole has written The Enterprise Data Catalog (O’Reilly, 2023). He is currently working on Fundamentals of Metadata Management (O’Reilly, 2025), introducing a novel metadata architecture known as the Meta Grid. With a PhD in Library and Information Science from the University of Copenhagen, his unique perspective bridges traditional information science with modern data management.

Before joining Actian, Ole served as Chief Evangelist at Zeenea, where he played a key role in shaping and communicating the company’s technology vision. His industry experience includes leadership roles in enterprise architecture and data strategy at major pharmaceutical companies like Novo Nordisk.Ole is passionate about scalable metadata architectures, knowledge graphs, and enabling organizations to make data truly discoverable and usable.

Connect with Ole online

LinkedIn

Substack

Medium

Resources mentioned in this interview

Fundamentals of Metadata Management, Ole's forthcoming book

Data Management at Scale by Piethein Strengholt

Fundamentals of Data Engineering by Joe Reis and Matt Housley

Meta Grid as a Team Topology, Substack article

Stewart Brand's Pace Layers

Video

Here’s the video version of our conversation:

https://youtu.be/t01IZoegKRI

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 28. Every modern enterprise wrestles with the scale, the complexity, and the urgency of understanding their data and metadata. So, by necessity, comprehensive architectural approaches like microservices and the data mesh are complex, big, and fast. Ole Olesen-Bagneux proposes a simple, small, and slow way for enterprises to cultivate a shared understanding of their enterprise knowledge, a decentralized approach to metadata strategy that he calls the Meta Grid.

Interview transcript

Larry:

Hi, everyone. Welcome to episode number 28 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Ole Olesen-Bagneux. Ole is the... He's currently the chief evangelist at Actian. Welcome, Ole. Tell the folks a little bit more about your role at Actian and what you do there.

Ole:

Thank you, Larry. Thank you for having me on. This is a great topic that we will dive in today. First of all, I recently joined Actian, so it's been my first week since we've recorded this. I am the chief evangelist in Actian, so I will be telling the story of Actian as this technology evolves. Actian is a data platform, it's based in the US and it is part of the HCL software family. Actian is a data platform, and as this data platform changes and evolves over time, I'll be telling that story. That's really what I'll be doing.

Larry:

What a fun job, evangelizing a technology platform. Yeah. I love that kind of work. You're also working on a book called Fundamentals of Metadata Management, which is all about the Meta Grid concept that you came up with, and that's what I really want to talk about today. How's the book going? It's due for publication this year, correct?

Ole:

That is correct. I finalized the first draft version of the manuscript a couple of weeks ago. I am very confident that I will publish it on time. Maybe a little earlier than the current publication date, that I don't know, but it's coming along nicely. It's been a difficult book to write. It's the sum of my experience as a leader and an enterprise architect working closely with all things data for the last 10, 15 years. 10 years.

Larry:

Nice. Sort of a encapsulation. Well, it's an intriguing idea, and I love the... We were talking before we went on the air about how much I love a good framework, and is that the right way to think about it? I guess, how would you define the Meta Grid?

Ole:

The Meta Grid, as I see it, is first and foremost an architecture, an integration architecture between distinct types of technologies that do not perform the value chain of a company such as ERP systems and CRM systems and what have you. Neither is it focused at the analytical plane, so to say, the data mesh universe where you're trying to discuss how centralized or decentralized should your analytical data technologies be at data warehouses, data lakes, and the like. The Meta Grid really is about these small, small technologies that depict the IT landscape, the information security management system, the data catalog, the endpoint management system, the knowledge management system, all these systems that for distinct purposes depict the IT landscape. A short definition of the Meta Grid is that it is an integration architecture that connects metadata across metadata repositories.

Larry:

Nice. That connects across repositories, and that's one of the key things about the Meta Grid is that your conception of metadata differs... Well, you come out of a library science background, isn't that right?

Ole:

That is right.

Larry:

I've learned most of what I know about metadata from my librarian friends, but I've also... I think I've fallen into the not trap or a different way of thinking about it than you do this notion of just thinking about the kinds of metadata, descriptive metadata, administrative, tech, all those kinds. You have a different take on metadata. I'd love to hear how you think of it?

Ole:

You bet. Yes. Well, first of all, let me emphasize that I don't think the way you think of metadata is wrong. That's quite important for me. But what I see lacking in that thinking, for example, in the Data Management Body of Knowledge, the DAMA-DMBOK or other resources, vital resources in this field is that if you do that subcategorization of metadata into operational, technical, business, social, what have you, then you discuss subcategories instead of the actual nature of metadata. What I've come to find in my work life is that this perspective gives you some blind spots to something that is very, very important in metadata. How I define metadata is not in terms of its subcategories. I don't think you can do that actually for metadata. I try to find the very nature of metadata in my definition of it, and that is that it is not anything in particular, but that it is always in two places at once.

Ole:

That's the key to metadata. It'll be in a repository and it will be at source, just like a book has its information, its metadata in the book itself and on Amazon. Just like the data in a data catalog is listed in the data catalog and in the data sources of that particularly data source that you have scanned. Metadata is in two places at once, and that definition unfolds the idea of the Meta Grid because once you have established this definition, you can also look at this definition as a problem because what it creates, and you can see that at every single company in the world, is that you have a lot of different metadata repositories listing the same type of metadata. That's the problem that the Meta Grid addresses, that all these small activities in all these small teams are taking place in isolation.

Larry:

Is that the classic silo issue in enterprise architectures?

Ole:

It's definitely the classic silo issue in enterprise architecture just for a very distinct purpose. I feel very, very connected to everything, team topologies, everything data mesh, microservices. These ideas of thinking of how to break up monolithic thinking and monolithic technology practices is something that is very, very close to... Well, to my heart and my mind.

Larry:

That close to mine as well. I help organize a conference called Decoupled Days, it's all about microservices and decoupled architectures, but I love that you... But one of your... I read a bunch of stuff preparing for this, but you did this one blog post where you had a table that compares the microservices architectures, the data mesh,...

Andrea Volpini

Your organization's brand is what people say about you after you've left the room. It's the memories you create that determine how people think about you later.

Andrea Volpini says that the same dynamic applies in marketing to AI systems. Modern brand managers, he argues, need to understand how both human and machine memory work and then use that knowledge to create digital memories that align with how AI systems understand the world.

We talked about:

his work as CEO at WordLift, a company that builds knowledge graphs to help companies automate SEO and other marketing activities

a recent experiment he did during a talk at an AI conference that illustrates the ability of applications like Grok and ChatGPT to build and share information in real time

the role of memory in marketing to current AI architectures

his discovery of how the agentic approach he was taking to automating marketing tasks was actually creating valuable context for AI systems

the mechanisms of memory in AI systems and an analogy to human short- and long-term memory

the similarities he sees in how the human neocortex forms memories and how the knowledge about memory is represented in AI systems

his practice of representing entities as both triples and vectors in his knowledge graph

how he leverages his understanding of the differences in AI models in his work

the different types of memory frameworks to account for in both the consumption and creation of AI systems: semantic, episodic, and procedural

his new way of thinking about marketing: as a memory-creation process

the shift in focus that he thinks marketers need to make, "creating good memories for AI in order to protect their brand values"

Andrea's bio

Andrea Volpini is the CEO of WordLift and co-founder of Insideout10. With 25 years of experience in semantic web technologies, SEO, and artificial intelligence, he specializes in marketing strategies. He is a regular speaker at international conferences, including SXSW, TNW Conference, BrightonSEO, The Knowledge Graph Conference, G50, Connected Data and AI Festival.

Andrea has contributed to industry publications, including the Web Almanac by HTTP Archive. In 2013, he co-founded RedLink GmbH, a commercial spin-off focused on semantic content enrichment, natural language processing, and information extraction.

Connect with Andrea online

LinkedIn

X

Bluesky

WordLift

Video

Here’s the video version of our conversation:

https://youtu.be/do-Y7w47CZc

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 27. Some experts describe the marketing concept of branding as, What people say about you after you’ve left the room. It's the memories they form of your company that define your brand. Andrea Volpini sees this same dynamic unfolding as companies turn their attention to AI. To build a memorable brand online, modern marketers need to understand how both human and machine memory work and then focus on creating memories that align with how AI systems understand the world.

Interview transcript

Larry:

Hi, everyone. Welcome to episode number 27 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Andrea Volpini. Andrea is the CEO and the founder at WordLift, a company based in Rome. Tell the folks a little bit more about WordLift and what you're up to these days, Andrea.

Andrea:

Yep. So we build knowledge graphs and to help brands automate their SEO and marketing efforts using large language model and AI in general.

Larry:

Nice. Yeah, and you're pretty good at this. You've been doing this a while and you had a recent success story, I think that shows, that really highlights some of your current interests in your current work. Tell me about your talk in Milan and the little demonstration you did with that.

Andrea:

Yeah, yeah, so it was last week at AI Festival, which is a very large event with I would say hundreds of speakers. And my talk was about memory as a new framework for marketing in the age of AI assistant. And so I did a small test with the audience and I imagine we had a crowd of maybe, I don't know, 40, 60 people attending the talk and a few others online. And I had these slides where I challenged the audience to program the memory of Grok. Grok is X AI system. And I wanted to do this with Grok and ChatGPT by asking the audience to share feedback about my talks. The talks was ready towards the end. And so I asked, "Okay, just share openly on X and Facebook about how was this talk?" And then we set up a small poll on X to let people simply vote if it was good or bad or relevant or boring.

Andrea:

And so we created engagement over social and of course, particularly because I'm still one of the few left on X, we interacted on X. And then all of a sudden, maybe after just a few minutes, one of my colleague went on Grok and asked, "What are the best talks at AI festival 2025?" And you can imagine there are hundreds of speakers, but Grok responded, "One highlight is a CyberAndy presentation that talked about using memory with AI system, and one of the attendees described it as mesmerizing, suggesting that he explored neuroscience," and blah, blah, blah. So I was able to get there and to build memory collectively by having user share feedback on social network. And by the way, the same applied to ChatGPT. So asking the same to ChatGPT would also highlighted my talk versus many others, better talks on that day.

Larry:

That's really one of the common observations and criticisms of LLMs has been their inability to access real-time information. That you build the model and there it is. So there's obviously something going on under there. You're one of the first people I've talked to who talks a lot about memory in these architectures. I guess, maybe if you could, I mean there's so much going on in the last couple of years with this, but what have been the evolutions in the AI and LLM sphere that kind of have led to the emerging importance of memory in these architectures?

Andrea:

So I mean, I think all of us are realizing with daily use that we're not interacting with language models anymore, but we are interacting with more complex systems that take into account multiple pieces in order to provide an accurate response. And every system, whether we're dealing with Perplexity, ChatGPT, or Gemini, or Grok has its own different way of combining information in order to respond to us.

Andrea:

And so I started, because my work in marketing, I started to think how we should approach a customer that is becoming an AI. And then that was my trigger was like, okay, what if the next customer is not a human? What happens? And the first consideration to be made is that in the context of SEO, for example, we transition with after a few years from the idea of keywords and focusing on what are the keywords that I should rank for to focusing on the search intent of the user that makes a request to a search engine. But then all of this is gone, if I have to deal with ChatGPT, Deep Search, all of these disappear if I have to deal with something like Operator or Gemini Deep Research functionality because in the end there's not going to be a human that it's making the request, but it's going to be an agent. And so I started to think, okay, what is marketing then if keywords are gone and also search intents is gone, what is left? What influenced the systems? And then I got to the revelation of memory.

Larry:

Okay. That's really interesting. The way, that evolution you just described too. The one thing that occurred to me as you were talking about that is that ostensibly Google has always favored that if you're doing things that appeal to human beings, you'll rank better in the search engines. But it sounds like from what you're saying, and so that kind of guided SEO for the last, I don't know, 15, 20 years, but now you're saying we're in this, we've kind of switched to where, and so I think a lot of SEOs, the perception was they were just playing to Google to trying to game Google's algorithms. And it's not like gaming, but it's understanding your audience. It's like any old communication problem, understanding your audience.

Larry:

So what are you seeing as the difference as you make that leap from search intent to memory needs of these new like Deep Research and tools like that? How do you, and your end goal in this is to automate marketing tasks. What does that look like? What's the pipelines or procedures or your approach to that?

Andrea:

So I started from building our system for our client to let's say improve the quality of content recommendation on an e-commerce website or increasing the quality of internal links and doing that at scale required an agentic approach. So there is a language model driven agent that has to find relevant pages and then has to have the notion of what is a main query for these pages, and then as to learn how to craft a proper anchor text in order to link one page to a relevant other page.

Andrea:

So as I was doing this development, I realized that the essence wasn't really the model itself. That of course has its own characteristics and biases, but it was really the context that I was feeding the model with in real time in order for it to do the task. And so I realized that a pivotal change, it's on how we craft these memories. What is the information in context that we want to pass to the agent in order to do the task properly, and how does the system evolve as things move forward and user maybe start clicking on these links and search engines start crawling these pages. And so I realized that memory was really the underlying element of success for my AI agents.

Larry:

So memory, and when we think of memory, you think of RAM and the computer memory, but also human memory and the different kinds of memory like short-term, long-term....

Jacobus Geluk

The arrival of AI agents creates urgency around the need to guide and govern them.

Drawing on his 15-year history in building reliable AI solutions for banks and other enterprises, Jacobus Geluk sees a standards-based data-product marketplace as the key to creating the thriving data economy that will enable AI agents to succeed at scale.

Jacobus launched the effort to create the DPROD data-product description specification, creating the supply side of the data market. He's now forming a working group to document the demand side, a "use-case tree" specification to articulate the business needs that data products address.

We talked about:

his work as CEO at Agnos.ai, an enterprise knowledge graph and AI consultancy

the working group he founded in 2023 which resulted in the DPROD specification to describe data products

an overview of the data-product marketplace and the data economy

the need to account for the demand side of the data marketplace

the intent of his current work on to address the disconnect between tech activities and business use cases

how the capabilities of LLMs and knowledge graphs complement each other

the origins of his "use-case tree" model in a huge banking enterprise knowledge graph he built ten years ago

how use case trees improve LLM-driven multi-agent architectures

some examples of the persona-driven, tech-agnostic solutions in agent architectures that use-case trees support

the importance of constraining LLM action with a control layer that governs agent activities, accounting for security, data sourcing, and issues like data lineage and provenance

the new Use Case Tree Work Group he is forming

the paradox in the semantic technology industry now of a lack of standards in a field with its roots in W3C standards

Jacobus' bio

Jacobus Geluk is a Dutch Semantic Technology Architect and CEO of agnos.ai, a UK-based consulting firm with a global team of experts specializing in GraphAI — the combination of Enterprise Knowledge Graphs (EKG) with Generative AI (GenAI). Jacobus has over 20 years of experience in data management and semantic technologies, previously serving as a Senior Data Architect at Bloomberg and Fellow Architect at BNY Mellon, where he led the first large-scale production EKG in the financial industry.

As a founding member and current co-chair of the Enterprise Knowledge Graph Forum (EKGF), Jacobus initiated the Data Product Workgroup, which developed the Data Product Ontology (DPROD) — a proposed OMG standard for consistent data product management across platforms. Jacobus can claim to have coined the term "Enterprise Knowledge Graph (EKG)" more than 10 years ago, and his work has been instrumental in advancing semantic technologies in financial services and other information-intensive industries.

Connect with Jacobus online

LinkedIn

Agnos.ai

Resources mentioned in this podcast

DPROD specification

Enterprise Knowledge Graph Forum

Object Management Group

Use Case Tree Method for Business Capabilities

DCAT Data Catalog Vocabulary

Video

Here’s the video version of our conversation:

https://youtu.be/J0JXkvizxGo

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 26. In an AI landscape that will soon include huge groups of independent software agents acting on behalf of humans, we'll need solid mechanisms to guide the actions of those agents. Jacobus Geluk looks at this situation from the perspective of the data economy, specifically the data-products marketplace. He helped develop the DPROD specification that describes data products and is now focused on developing use-case trees that describe the business needs that they address.

Interview transcript

Larry:

Okay. Hi everyone. Welcome to episode number 26 of the Knowledge Graph Insights podcast. I am really happy today to welcome to the show, Jacobus Geluk. Sorry, I try to speak Dutch, do my best. His last name means happiness in Dutch, which you'll see a lovely bit of serendipity here. Jacobus is the CEO at Agnos.ai, which is a really prominent enterprise knowledge graph consultancy based in London. So welcome Jacobus, tell the folks a little bit more about what you're doing these days.

Jacobus:

Oh, thank you very much, Larry, for the opportunity. Well, we are a small, let's say, boutique consulting company, but focusing on the enterprise knowledge graph in combination with gen AI, LLM, of course. I think that's a match made in Heaven, these two technologies, they need each other and we jump on that completely all in because I think that's the future. And yeah, would love to talk about this topic-

Larry:

Yeah, well there's one topic in particular we've been talking about that I really want to air out today is the notion of both of those, all of AI, it runs on data and there's this emerging thing, the data product that is like, I don't know how clearly articulated it is, but you've worked on the DPROD initiative working on articulating an ontology for what a data product is. So you know as much about it as anybody. So what I'd love to do is just talk about, well first, what is a data product? For folks who aren't familiar or discovering this for the first time, how would you describe a data product?

Jacobus:

Yeah, well, we started talking about this, I set this up in September 2023, and I started talking to Tony Seale, who's a very famous, he's the The Knowledge Graph Guy. You'll find him on LinkedIn. He writes fantastic articles. And at that time, he worked on the largest knowledge graph project in the financial industry, I think at the moment, which is one of the largest which was at UBS. And I asked him to become the chair of a new work group that they wanted to set up in the context of the Enterprise Knowledge Graph Forum, which is part of the Object Management Group, which is a standards organization like W3C. And to my surprise, almost it went so well that within a year we basically hammered out this standard, or it's now an official OMG standard called DPROD that stands for the data product ontology.

Jacobus:

And we work with many, many people mostly from banks like JP Morgan and some other people from UBS, Credit Suisse was involved, London Stock Exchange Group, Bloomberg, you name it. Like a whole range of people, Amazon, British Telecom. So there's multiple different types of people working on it: specialists, data architects, et cetera. And the idea was we want to see the world moves towards data marketplaces, like large companies like London Stock Exchange Group for example, or Azure, Google, et cetera. They host data or they sell data. They are data vendors in a sense. So if you sell something, what is your product? Your data is a product or access to that data is a product. So rather than modeling things in terms of data sets, et cetera, which is what we did so far, that is the state-of-the-art data, cataloging, et cetera, using a standard called DCAT that is basically the data catalog standard.

Jacobus:

So we thought, "Okay, let's elevate that a little bit higher." We don't want to think in terms of pure data sets anymore. That's kind of more like an internal thing. The customer doesn't really care. The customer just wants to know, "What is your product, how can I access it, how can I buy it? What are the terms and conditions? What is the purpose? What are the use cases that we can support," et cetera. So I'm not claiming that we have all of that, but we have at least created a one step up from DCAT, literally an extension to DCAT that basically says, "Okay, now we define what the data product is, what are the inputs and the outputs. Input ports, output ports, basically how does it fit into a larger supply chain of data?" And that is already a step forward and companies are using it.

Jacobus:

Like there's already several companies that have told us that they are actually using it like JP Morgan and London Stock Exchange Group, UBS, they are all using it. I'm not sure if it's already in production or not, but they worked with us to create this and there's apparently a need to model the world in terms of your data products.

Jacobus:

But my own story to that is, okay, you have products, but you would say instead of a data marketplace, you could also say it's a data economy. Basically you want to look at the world as a data economy, your own enterprise, but also beyond the enterprise. It's a larger data economy where you have supply and demand. So we have now defined all these data products on the supply side of the data economy, but what is the demand side? That there's no real standard yet, I think, that defines what the use cases are.

Jacobus:

Like if you want to talk to the business, the business has a problem, has a budget, and they want us to build something every time. So what is that? That is a use case. That's the term that they use. The business is talking about use cases or apps or systems or whatever you call it, but most of the time they use the term use case. So we want to create a new thing, basically defining the stuff on the demand side of that data economy called use cases. And these use cases can cannot only serve the purpose of defining very precisely what the business wants and needs, but also how that links to data products with data contracts in between et cetera.

Jacobus:

And last but not least, before I stop talking, also the LLM, the gen AI basically needs to know what is the use case I am supposed to operate in. So that's the most exciting angle to this story basically is we want to control the LLM agents, of course, and make them useful, productive, producing high quality output in production, in mission-critical use cases. But what are those use cases? We want those use cases to be data and it's data as code almost. But let's stop talking about this now.

Larry:

No,...

Rebecca Schneider

Skills that Rebecca Schneider learned in library science school - taxonomy, ontology, and semantic modeling - have only become more valuable with the arrival of AI technologies like LLMs and the growing interest in knowledge graphs.

Two things have stayed constant across her library and enterprise content strategy work: organizational rigor and the need to always focus on people and their needs.

We talked about:

her work as Co-Founder and Executive Director at AvenueCX, an enterprise content strategy consultancy

her background as a "recovering librarian" and her focus on taxonomies, metadata, and structured content

the importance of structured content in LLMs and other AI applications

how she balances the capabilities of AI architectures and the needs of the humans that contribute to them

the need to disambiguate the terms that describe the span of the semantic spectrum

the crucial role of organization in her work and how you don't to have formally studied library science to do it

the role of a service mentality in knowledge graph work

how she measures the efficiency and other benefits of well-organized information

how domain modeling and content modeling work together in her work

her tech-agnostic approach to consulting

the role of metadata strategy into her work

how new AI tools permit easier content tagging and better governance

the importance of "knowing your collection," not becoming a true subject matter expert but at least getting familiar with the content you are working with

the need to clean up your content and data to build successful AI applications

Rebecca's bio

Rebecca is co-founder of AvenueCX, an enterprise content strategy consultancy. Her areas of expertise include content strategy, taxonomy development, and structured content. She has guided content strategy in a variety of industries: automotive, semiconductors, telecommunications, retail, and financial services.

Connect with Rebecca online

LinkedIn

email: rschneider at avenuecx dot com

Video

Here’s the video version of our conversation:

https://youtu.be/ex8Z7aXmR0o

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 25. If you've ever visited the reference desk at your local library, you've seen the service mentality that librarians bring to their work. Rebecca Schneider brings that same sensibility to her content and knowledge graph consulting. Like all digital practitioners, her projects now include a lot more AI, but her work remains grounded in the fundamentals she learned studying library science: organizational rigor and a focus on people and their needs.

Interview transcript

Larry:

Hi, everyone. Welcome to episode number 25 of the Knowledge Graph Insights podcast. I am really excited today to welcome to the show Rebecca Schneider. Rebecca is the co-founder and the executive director at AvenueCX, a consultancy in the Boston area. Welcome, Rebecca. Tell the folks a little bit more about what you're up to these days.

Rebecca:

Hi, Larry. Thanks for having me on your show. Hello, everyone. My name is Rebecca Schneider. I am a recovering librarian. I was a trained librarian, worked in a library with actual books, but for most of my career, I have been focusing on enterprise content strategy. Furthermore, I typically focus on taxonomies, metadata, structured content, and all of that wonderful world that we live in.

Larry:

Yeah, and we both come out of that content background and have sort of converged on the knowledge graph background together kind of over the same time period. And it's really interesting, like those skills that you mentioned, the library science skills of taxonomy, metadata, structured, and then the application of that in structured content in the content world, how, as you've got in more and more into knowledge graph stuff, how has that background, I guess... what's been the transition like as you start to consider knowledge graphs in your content work? How's that been going?

Rebecca:

Well, I mean, librarians, we're all about organizing things, and we like to organize stuff, and this is just sort of the next step in helping organize stuff so people can find things. I mean, that's what we're all about, right? We want to help people find things. So moving into more and more sophisticated mechanisms that help people not only find things, but leverage content in new and different ways and useful ways is, I think, just a logical progression.

Larry:

Yeah, that's really... that content discovery, like finding things, discovery, and uncovering things, that's sort of always been the information architecture-y part of content practice. But how is that changing? We were talking a little bit before we went on the air about the emergence of LLMs and then the ensuing interest in knowledge graphs associated with that. Are you finding different challenges in helping machines discover stuff as opposed to humans?

Rebecca:

Well, okay, there's a couple of different aspects to that. One is that, as we know, structured content is so very important for the success of LLMs, AI applications, et cetera. And the thing is, I have to convince my clients to take the time to clean their house, so to speak, and say, "Okay, you need to clean up your data. You need to clean up your content. You need to get rid of the old, outdated, trivial, extraneous stuff so you have a clean basis to start from." And convincing clients to take that time is a bit of a struggle because they see all the fancy LLMs and all different wonderful things people are doing, and they just want to jump in with both feet, which is great, but you need to have a solid basis first. So it takes some convincing to have people take the time to do that.

Rebecca:

And then on the LLM side, we have to acknowledge that there are many different kinds of LLMs with different pros and cons depending on what the use case is, and you need to not only understand the LLMs, but also the organization's business drivers, what are their goals, what are their objectives, what are they trying to do with this? Because it's not just to write me a poem in the style of e.e. cummings about apples or something. There are definite business drivers and goals because a lot of money is putting a lot of funds into these kinds of technologies, and you got to make sure that you're getting your bang for your buck.

Larry:

Yeah. As you say that, you're reminding me that everybody's wrestling, you're not wrestling, but figuring out how to do work with RAG architectures and graph RAG and all these hybrid AI architectures. And I love the way that it sounds like your work... you start with the business goals and kind of back up from there, which seems like a good approach. Are you discovering any patterns or approaches as you figure out how do you balance... because that's an interesting triangle of needs, the business part of the client needs. There's sort of content needs, and then the capabilities of the LLMs and the other tooling and these architectures. That's got to be a lot, it sounds like.

Rebecca:

It is, and you also have to think about how much work am I going to make people do. You can't train everybody to be prompt engineers, right? And so you need to think about from their perspective, what they're trying to get out of whatever application you're creating, and also their interaction with it to the extent to which, yes, the information is in there, but you have to ask better questions. Does that mean I have to retrain people on how to ask better questions? To what extent is that necessary, or are there other ways of leveraging how we use an LLM in this sort of hybrid structure? How can we use ontologies, et cetera, informing the knowledge graph to help the user so they don't have to be prompt engineers, that they can get what they need with the minimum of fuss.

Larry:

Yeah, that's really interesting. As you say that, using ontologies to inform the knowledge graph, I was just kind of assumed in my head that ontologies were a prerequisite for knowledge graphs, but I had Jessica Talisman on a while back, and she makes the case that you can judge a lot just with a SKOS-based thesaurus. Do you consider that kind of continuum of semantic sophistication from just term lists to taxonomies to thesauri to full-blown ontologies, are you playing across that spectrum in your work?

Rebecca:

Yeah, absolutely, absolutely. And everybody... not everybody. A lot of people say, well, taxonomy, and they use it to refer to controlled vocabularies, ontologies, et cetera. To them, it's all taxonomy. It's not, actually. It is a spectrum. It is a progression of, okay, I've got my control vocabulary, then I have hierarchical structure, and then I have synonyms and use-for and all of that kind of stuff. And then you have the ontology. So it's definitely to my mind, a spectrum. And sometimes you don't need the full-blown... I agree. You don't need the full-blown ontology. You can do a lot with a well-structured, well-thought-out taxonomy in an SKOS architecture, and you can really leverage that and you might not need to go the full ontology route. Baby steps.

Larry:

Yeah. Well, I think baby steps, that's a lesson that comes up everywhere. And that's it. And some of those baby steps, so how... I've talked to a lot of people about this from different backgrounds, and this seems to come really naturally to librarians, or people out of a library science background, that comfort with that spectrum of options for how to organize things is that's kind of a... I didn't go to library science school. I've hung out with you all a lot. But is that sort of just a part of the mindset of a librarian?

Rebecca:

Yeah, I mean, a lot of people actually become librarians as a second career. When I went to school back in the day,...

Ashleigh Faith

With her 15-year history in the knowledge graph industry and her popular YouTube channel, Ashleigh Faith has informed and inspired a generation of graph practitioners and enthusiasts.

She's an expert on semantic modeling, knowledge graph construction, and AI architectures and talks about those concepts in ways that resonate both with her colleagues and with newcomers to the field.

We talked about:

her popular IsA DataThing YouTube channel

the crucial role of accurately modeling actual facts in semantic practice and AI architectures

her appreciation of the role of knowledge graphs in aligning people in large organizations around concepts and the various words that describe them

the importance of staying focused on the business case for knowledge graph work, which has become both more important with the arrival of LLMs and generative AI

the emergence of more intuitive "talk to your graph" interfaces

some of her checklist items for onboarding aspiring knowledge graph engineers

how to decide whether to use a property graph or a knowledge graph, or both

her hope that more RDF graph vendors will offer a free tier so that people can more easily experiment with them

approaches to AI architecture orchestration

the enduring importance of understanding how information retrieval works

Ashleigh's bio

Ashleigh Faith has her PhD in Advanced Semantics and over 15 years of experience working on graph solutions across the STEM, government, and finance industries. Outside of her day-job, she is the Founder and host of the IsA DataThing YouTube channel and podcast where she tries to demystify the graph space.

Connect with Ashleigh online

LinkedIn

IsA DataThing YouTube channel

Video

Here’s the video version of our conversation:

https://youtu.be/eMqLydDu6oY

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 24. One way to understand the entity resolution capabilities of knowledge graphs is to picture on old-fashioned telephone operator moving plugs around a switchboard to make the right connections. Early in her career, that's one way that Ashleigh Faith saw the power of knowledge graphs. She has since developed sophisticated approaches to knowledge graph construction, semantic modeling, and AI architectures and shares her deeply informed insights on her popular YouTube channel.

Interview transcript

Larry:

Hi, everyone. Welcome to episode number 24 of the Knowledge Graph Insights Podcast. I am super extra delighted today to welcome to the show Ashleigh Faith. Ashleigh is the host of the awesome YouTube channel IsA DataThing, which has thousands of subscribers, thousands of monthly views. I think it's many people's entry point into the knowledge graph world. Welcome, Ashleigh. Great to have you here. Tell the folks a little bit more about what you're up to these days.

Ashleigh:

Thanks, Larry. I've known you for quite some time. I'm really excited to be here today.

What about me? I do a lot of semantic and AI stuff for my day job. But yeah, I think my main passion is also helping others get involved, understand some of the concepts a little bit better for the semantic space and now the neuro-symbolic AI. That's AI and knowledge graphs coming together. That is quite a hot topic right now, so lots and lots of untapped potential in what we can talk about. I do most of that on my channel.

Larry:

Yeah. I will refer people to your channel because we've got only a half-hour today. It's ridiculous.

Ashleigh:

Yeah.

Larry:

We just talked for an hour before we went on the air. It's ridiculous. What I'd really like to focus on today is the first stage in any of this, the first step in any of these knowledge graph implementations or any of this stuff is modeling. I think about it from a designerly perspective. I do a lot of mental model discernment, user research kind of stuff, and then conceptual modeling to agree on things. But when you get into this world, you get much more into the implementation side of going from a conceptual model into logical, physical models as well. Can you just talk a little bit first just about why modeling is so important?

Ashleigh:

Yeah. Modeling is the way that you put any of your data into context. If you're looking at something as a human and you see a column of names, you can look at that and say, "Oh, I see that as a human name." But you don't know if they're a customer, are they staff, are they vendors. You have no idea unless there's a column header that tells you what that is. That is the most simplistic model, it's a list.

Ashleigh:

That's why modeling is so important is because if you don't have any context to what this data is, and what it means, and how you should interpret it, it basically means nothing to you. Then you can misinterpret it and that leads to all kinds of problems.

Larry:

Right. One of the things we talked about before we went on the air was, "What are facts? What is knowledge?" Because ostensibly, that's what we're ensconcing in these systems that are built with these conceptual models that we come up with. How do you discern that stuff?

Ashleigh:

Yeah. If you look at a lot of us in the semantic space look a lot at Wikidata. If you look at Wiki data, these things are called statements. A triple that defines something and makes a statement about the world. In my mind, I come from a very deep scholarly community background where we're doing a lot of research and you're trying to find corroborating evidence. In my mind, when you turn a statement into a fact, it is when in that point in time, what is the overwhelming acceptance of a certain stance or statement in the world based on corroborating evidence. Now there are competing opinions about things. There are disputes, especially when you're talking about scholarship. Some people, their findings are one thing, and findings are different in a different study.

Ashleigh:

But all of that comes down to is there enough corroborating evidence in one direction? When there are disputes, sometimes there's nuance that has been added to the original statement where that original statement is still now true, it's still accepted. But now, with this additional evidence, it gets even more nuanced, so you get more specific. You can really understand the nuances of that.

Ashleigh:

There is a half-life of facts, though. Because there was a point in time in the world where people, an overwhelming amount of people, thought the world was flat. We all know that is not the case. But at the time, that was a fact because there was no other evidence to support it otherwise. Or there was very little, or it wasn't disseminated appropriately because it was quite some time ago. But that's where it all comes down is can you defend? If you're in a court of law, how do you defend your statements? How do you defend your argument? This is how scholarship has done things since the very beginning of time. Give me your evidence, go do some studies, and go and do some experiments to gather evidence to support your claim. That's what a hypothesis is doing, is can you support your hypothesis? Can you disclaim it? That's how you figure out what a statement and a fact is.

Ashleigh:

Then when you have that, how do you codify it so that it can be even more useful? That's where knowledge graphs really come in because you can create it in a way that you can do inferencing. I know that's a weighted statement nowadays. Unfortunately, it's a weighted statement. Oh, actually, even saying it's a weighted statement is problematic. I should stop while I'm ahead on that one.

Larry:

Yeah. Okay, we can change it. No.

Ashleigh:

Yeah. No, no. You can add in though that data governance aspect, especially if you're using an ontology. There's all these other aspects of why taking statements, and facts, and all of that evidence, and adding it into a system of record that you can then do other things with is really, really helpful, especially in this AI world.

Larry:

Yeah. That notion of system of record, for some people that's enterprise lingo. I love that you're talking about it. I love the academic and intellectual rigor that underlies your approach to modeling. I think a lot of the facts and knowledge that people are ensconcing in systems in the knowledge graph world, there's just a lot of enterprise stuff going on. What are the facts, and evidence, and corroboration that you need? Like the classic thing that comes up in every conversation. When you say "customer," what do you mean?

Ashleigh:

Yeah.

Larry:

How do you ensconce that organizational disparity in understanding of that term?

Ashleigh:

Oh, but that's why I love knowledge graphs for this! You can have your data catalog and you can have your taxonomies. You can attempt to get everyone in your organization to agree on what a specific label means. In my practical experience, all those things are great and lovely. And yes, do those if you can do do those. Often times though, and this is what I learned very early on in my career when I was first doing taxonomy work, was nobody ever agrees on what label is used for certain things. Or very few. There's always, "Well, there's that exception to the rule in our enterprise and it's super critical to our mission," and whatever else it might be. So you have that going on.

Ashleigh:

That's why I really love the very early examples where knowledge graph was used as a connector. I almost imagine it as those really old-school operators that would connect people with phone lines. You know, sitting there with the giant connector boards. That's what I think about when I think of the early examples that I was using with knowledge graph, which is you have a node and it represents this concept. That concept can be represented by an ID, so it does not need interpretation. It is that is the ID. That is the UID,...

Panos Alexopoulos

Any knowledge graph or other semantic artifact must be modeled before it's built.

Panos Alexopoulos has been building semantic models since 2006. In 2020, O'Reilly published his book on the subject, "Semantic Modeling for Data."

The book covers the craft of semantic data modeling, the pitfalls practitioners are likely to encounter, and the dilemmas they'll need to overcome.

We talked about:

his work as Head of Ontology at Textkernel and his 18-year history working with symbolic AI and semantic modeling

his definition and description of the practice of semantic modeling and its three main characteristics: accuracy, explicitness, and agreement

the variety of artifacts that can result from semantic modeling: database schemas, taxonomies, hierarchies, glossaries, thesauri, ontologies, etc.

the difference between identifying entities with human understandable descriptions in symbolic AI and numerical encodings in sub-symbolic AI

the role of semantic modeling in RAG and other hybrid AI architectures

a brief overview of data modeling as a practice

how LLMs fit into semantic modeling: as sources of information to populate a knowledge graph, as coding assistants, and in entity and relation extraction

other techniques besides NLP and LLMs that he uses in his modeling practice: syntactic patterns, heuristics, regular expressions, etc.

the role of semantic modeling and symbolic AI in emerging hybrid AI architectures

the importance of defining the notion of "autonomy" as AI agents emerge

Panos' bio

Panos Alexopoulos has been working since 2006 at the intersection of data, semantics and software, contributing in building intelligent systems that deliver value to business and society. Born and raised in Athens, Greece, Panos currently works as a principal educator at OWLTECH, developing and delivering training workshops that provide actionable knowledge and insights for data and AI practitioners. He also works as Head of Ontology at Textkernel BV, in Amsterdam, Netherlands, leading a team of data professionals in developing and delivering a large cross-lingual Knowledge Graph in the HR and Recruitment domain. Panos has published several papers at international conferences, journals and books, and he is a regular speaker in both academic and industry venues. He is also the author of the O’Reilly book “Semantic Modeling for Data – Avoiding Pitfalls and Dilemmas”, a practical and pragmatic field guide for data practitioners that want to learn how semantic data modeling is applied in the real world.

Connect with Panos online

LinkedIn

Video

Here’s the video version of our conversation:

https://youtu.be/ENothdlfYGA

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 23. In order to build a knowledge graph or any other semantic artifact, you first need to model the concepts you're working with, and that model needs to be accurate, to explicitly represent all of the ideas you're working with, and to capture human agreements about them. Panos Alexopoulos literally wrote the book on semantic modeling for data, covering both the principles of modeling as well as the pragmatic concerns of real-world modelers.

Interview transcript

Larry:

Hi everyone. Welcome to episode number 23 of the Knowledge Graph Insights podcast. I am really excited today to welcome to the show Panos Alexopoulos. Panos is the head of ontology at Textkernel, a company in Amsterdam that works on knowledge graphs for the HR and recruitment world. Welcome, Panos. Tell the folks a little bit more about what you're doing these days.

Panos:

Hi Larry. Thank you very much for inviting me to your podcast. I'm really happy to be here. Yeah, so as you said, I'm head of ontology at Textkernel. Actually, I've been working in the field of data semantics, knowledge graph ontologies for almost now 18 years, even before the era of machine learning, back when it was mostly about symbolic AI. Yeah, I've been working a lot on this field. I've seen its ups and downs, I've seen it's good and bad things, and I think our discussion is going to focus on these things. What I've been doing lately now with the field of AI, and I think... No, let me say this differently. I think that the field of data semantics, even in the era of AI and large language models, et cetera, is even more important and this is something that I'm actively looking now. I'm actually looking a lot on the synergy and in the interrelation between large language models with data, with knowledge graphs and ontologies.

Larry:

Yeah, I'd love to talk more about that because that just seems to be in the air. One thing that I want to talk about, and I realized I totally left out of my intro, that you wrote this brilliant book called Semantic Modeling for Data.

Larry:

That's right. I kind of buried my lead there, as we say in journalism, but one of the first things that, and we talked about this a little bit before we went on the air. Can you describe to folks what semantic modeling is? What are we doing there when we're modeling?

Panos:

Yes. So the definition I give to the term of semantic modeling is the practice of building descriptions of data that have three important characteristics. The first thing, the first characteristic is that this description should be accurate, that this we should describe data and domains in a correct way, right? We don't want to have statements and assertions that are wrong. The second characteristic is that these descriptions should be explicit both for people and machines. What does that mean? If I have a data, if I have a data set, a set of data and I give it to you, and when you read it, you cannot understand what it is about. That's not good semantics, right? The meaning is lost, and the same applies for systems, for machines. If I call an API, I take some data back and my machine, enterprise system is not able to interpret the meaning of this data, then I have an issue.

Panos:

The third characteristic is agreement. It's not enough to have explicit meaning on data. It's also very important that we both agree on the validity of that meaning and that we serve the same meaning, right? And it starts with, I can give as example very simple things like what is a knowledge graph? If you go and you try to find a definition of what a knowledge graph, you will see many definitions that are not necessarily consistent to each other, right? So there's already disagreement there, and actually the word of a good ontology, semantic modeler would be to try to start with defining that. So that's what semantic modelers do and the artifacts that we build, this is practically an umbrella type, semantic modeling that covers a lot of artifacts that we build. These artifacts can range from database schemas, taxonomies, hierarchies, glossaries, thesauri, ontologies as many of our audience already heard, knowledge graph, et cetera.

Panos:

So when you do semantic modeling, you're not building necessarily one type of artifact. Then the key to remember is that you are describing data, you're describing domains by means of formal symbolic representation. That's also another important thing that semantic data modeling is about creating explicit human understanding, not only system understandable, but also human understandable descriptions of data. That means that embeddings, large language model, et cetera, do not fall into this category because their representations, their underlying representation are subsymbolic, there are numbers, so they maybe contain some meaning. They may be encoding some meaning, but according to my definition, they do not fall into the practice of semantic data modeling.

Larry:

Got it. The way you just said that sub-symbolic, because I've heard a lot of people talk about the symbolic AI and neural networks or machine learning and other, the sub-symbolic stuff. Is there a distinct dividing line there or is this a continuum going up and down then the AI stuff?

Panos:

For me, I don't think it's a continuum. Sub-symbolic is when you have numbers practically, right? So when you have a word, and this is encoded by a vector of 1600 or so numbers, this representation is subsymbolic. Symbolic means words, symbolic means human language, right? Terminology that doesn't happen in machine learning.

Larry:

Got it. Yeah, no, as soon as you said that, I was like, "It's more of a Boolean." Like, "Yep, it's symbolic or it's not."

Panos:

Of course of you can have, let's say hybrid models. For instance, you can take a knowledge graph of entities and the attributes of each entity. You can have the name of the entity, some other characteristics, and then you may have also an embedding of that entity. So you can have at the same time, two representations, a symbolic one and a subsymbolic one for the same entity, and you use that for different purposes. For example, an embedding is very good to find similarities between entities, which is more difficult to do it in the symbolic space.

Larry:

Interesting, yeah. And this gets into RAG architectures and other hybrid architectures that are emerging, or does it? Is that how those come together that if you're looking for similarities, you revert to like a subsymbolic system, but if you're looking for, I don't know, knowledge-based stuff, you shift into the symbolic.

Panos:

Yeah, so RAG is a nice example, right? So what's the idea of RAG? RAG stands for Retrieval Augmented Generation. It's the idea that you have your LLM, but you want it to give you answers based on your own data, based on your own knowledge. So when you give a prompt, when you give a query to the LLM before the LLM answers, you have a step where you retrieve relevant information to your query from some database, from some other set of documents, or as it's lately more into vogue structured data and knowledge graphs....

Mike Pool

Mike Pool sees irony in the fact that semantic-technology practitioners struggle to use the word "semantics" in ways that meaningfully advance conversations about their knowlege-representation work.

In a recent LinkedIn post, Mike even proposed a moratorium on the use of the word.

We talked about:

his multi-decade career in knowledge representation and ontology practice

his opinion that we might benefit from a moratorium on the term "semantics"

the challenges in pinning down the exact scope of semantic technology

how semantic tech permits reusability and enables scalability

the balance in semantic practice between 1) ascribing meaning in tech architectures independent of its use in applications and 2) considering end-use cases

the importance of staying domain-focused as you do semantic work

how to stay pragmatic in your choice of semantic methods

how reification of objects is not inherently semantic but does create a framework for discovering meaning

how to understand and capture subtle differences in meaning of seemingly clear terms like "merger" or "customer"

how LLMs can facilitate capturing meaning

Mike's bio

Michael Pool works in the Office of the CTO at Bloomberg, where he is working on a tool to create and deploy ontologies across the firm. Previously, he was a principal ontologist on the Amazon Product Knowledge team, and has also worked to deploy semantic technologies/approaches and enterprise knowledge graphs at a number of big banks in New York City. Michael also spent a couple of years on the famous Cyc project and has evaluated knowledge representation technologies for DARPA. He has also worked on tooling to integrate probabilistic and semantic models and oversaw development of an ontology to support a consumer-facing semantic search engine. He lives in New York City and loves to run around in circles in Central Park.

Connect with Mike online

LinkedIn

Video

Here’s the video version of our conversation:

https://youtu.be/JlJjBWGwSDg

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 22. The word "semantics" is often used imprecisely by semantic-technology practitioners. It can describe a wide array of knowledge-representation practices, from simple glossaries and taxonomies to full-blown enterprise ontologies, any of which may be summarized in a conversation as "semantics." Mike Pool thinks that this dynamic - using a word that lacks precise meaning while assuming that it communicates a lot - may justify a moratorium on the use of the term.

Interview transcript

Larry:

Hi everyone, welcome to episode number 22 of the Knowledge Graph Insights podcast. I'm really happy today to welcome to the show Mike Pool. Mike is a longtime ontologist, a couple of decades plus. He recently took a position at Bloomberg. But he made this really provocative post on LinkedIn lately that I want to flesh out today, and we'll talk more about that throughout the rest of the show. Welcome, Mike, tell the folks a little bit more about what you're up to these days.

Mike:

Hey, thank you, Larry. Yeah. As you noted, I've just taken a position with Bloomberg and for these many years that you alluded to, I've been very heavily focused on building, doing knowledge representation in general. In the last let's say decade or so I've been particularly focused on using ontologies and knowledge graphs in large banks, or large organizations at least, to help organize disparate data, to make it more accessible, breakdown data silos, et cetera. It's particularly relevant in the finance industry where things can be sliced and diced in so many different ways. I find there's a really important use case in the financial space but in large organizations in general, in my opinion, for using ontology. So that's a lot of what I've been thinking about, to make that more accessible to the organization and to help them build these ontologies and utilize them effectively.

Larry:

Nice. One of the intellectual I guess foundations of that kind of practice is what we call semantics. Anyhow, I want to read part of that post you made on LinkedIn, which started a great conversation. One of the things you suggested, "I think we need to impose a moratorium on the use of the word semantics. The reason is simple, it's ironically a term lacking any precise meaning while we assume it's communicating a lot." That's brilliant, can you elaborate on that a little bit? Was there a particular, did something inspire that or has it just been on your mind?

Mike:

Yeah. I mean, it's mostly, one term that often triggers it for me is this term I see within, let's call it this community of practice ... I see used very, very frequently, people will say, "Let's look at the semantic meaning." So this redundancy in terms, that we said, "Well, what in the world?" But we use it for all kinds of things. We say we need a semantic solution, we need the semantic meaning. And very often what that ends up being when we drill into that, it's just not always clear. The term I think in some sense has become either too vague ... it's unclear of what precisely it means. Or it's a shorthand for something else, that we're not actually saying we're going to capture meaning. We're saying, we're going to use this particular set of tools or something like that. So my concern is that it's sort of lost. We know when we say it that we mean we're going to use these particular set of tools, these particular set of languages, but to the people with whom we're communicating that might remain completely unclear. So yeah, that's my concern about the way we're using the concept.

Larry:

Yeah. That's really interesting that ... it's not laziness, it's like heuristics or something like that, that people use all the time to just try to advance whatever conversation they're in or project or whatever. It's just like, "Oh yeah, we need a semantic thing there," or something like that, it sounds like. Or they're thinking of possibly 20 different things or they just say, "Oh, semantic is the closest word I know to that idea," that we need to advance this.

Mike:

Yeah. I mean, an example is ... Because I think, as I noted somewhat ironically, I herald myself as a semantic technology practitioner or something like that. After you said to me, "Well, what in the world is semantic technology?" It's a good question. If I create a property graph, there's part of me that says, well, that's not really semantic but a triple store is. It's like, well, what's the dividing line? What precisely makes it count as semantic or not? It's a little bit hard to pin that down.

Larry:

The way you just said it, it's almost like there's an on/off switch someplace. But I've seen, there's a lot of representations of what various people have called something like the semantic spectrum, from just term lists to glossaries to thesauri, to the ontologies, that kind of thing. It's easy enough to disambiguate between each of those things I just mentioned, but is there something like a spectrum in there? Is that why people are grasping for words, do you think, to describe exactly what they're talking about in the moment?

Mike:

Yeah. As I said, I think that's part of the problem. As I said, the people with whom I often communicate, I think we more or less mean it as a shorthand for using RDF OWL. And that might be as simple as using SKOS and creating a simple taxonomy with that, or creating a very elaborate OWL ontology. But it's interesting because, let's say, we create a taxonomy in SKOS. Well, is there any reason that if you just had that taxonomy and you didn't bother to put it in SKOS, you just put it in an Excel spreadsheet with appropriate indentations? We'd say, well, how does the SKOS, or how does the RDF magically capture the meaning where the spreadsheet didn't, right? It's a little bit unclear. But I think other people use it differently. I think there's lots of people who would say using a property graph is a semantic solution. We're capturing knowledge, et cetera, in it. So I think it varies a little bit, that's again, part of the point. But I do believe people with whom I communicate, that's the shorthand we're using. It's like, this is either technology that we use to extend RDF OWL, or it's a knowledge graph that encodes that knowledge. But that's often what it means in my space I think.

Larry:

Also, you mentioned in that post too, I think, when you're talking about, if your intent is to capture meaning, that there are other ways to do that technologically. You talked about just an old-fashioned ERD or a graph schema or a Python script that captures something in some project you're working on. And even I think you also alluded to, or maybe it came up in the discussion, it could be a natural language thing that you say to an LLM that it could discern. But I guess that gets at, what is the amount of meaning you need to capture? Does that make sense? What's the intent behind your attempt to do something semantic in a technical project?

Mike:

It sounds like a straightforward question, but it's actually a very good one. Because as I said, that if we say, well, we're trying to capture meaning, you could write a Python script that does it, or there's a lot of different ways to do that. I think this whole, at least the background that I have in this, when we're talking about capturing semantics, what we are really concerned with is really trying to say, can we get a computer to reason in the same way that we do? Can we get the computer to respond to natural language prompts in a similar way that a human can? That's kind of what we meant by semantics. But then if we try to talk about that in the technology space, what exactly does that mean? Let's take the Python script example. If I said, oh, I'm trying to solve this problem, I have this search engine and every time people, they're searching for recipes,...

Margaret Warren

As a 10-year-old photographer, Margaret Warren would jot down on the back of each printed photo metadata about who took the picture, who was in it, and where it was taken.

Her interest in image metadata continued into her adult life, culminating the creation of ImageSnippets, a service that lets anyone add linked open data descriptions to their images.

We talked about:

her work to make images more discoverable with metadata connected via a knowledge graph

how her early childhood history as a metadata strategist, her background in computing technology, and her personal interest in art and a photography shows up in her product, ImageSnippets

her takes on the basics of metadata strategy and practice

the many types of metadata: descriptive, administrative, technical, etc.

the role of metadata in the new AI world

some of the good and bad reasons that social media platforms might remove metadata from images

privacy implications of metadata in social media

the linked data principles that she applies in ImageSnippets and how they're managed in the product's workflow

her wish that CMSs and social media platforms would not strip the metadata from images as they ingest them

the lightweight image ontology that underlies her ImageSnippets product

her prediction that the importance of metadata that supports provenance, demonstrates originality, and sets context will continue to grow in the future

Margaret's bio

Margaret Warren is a technologist, researcher and artist/content creator. She is the founder and CEO of Metadata Authoring Systems whose mission is to make the most obscure images on the web findable, and easily accessible by describing and preserving them in the most precise ways possible.

To assist with this mission, she is the creator of a system called, ImageSnippets which can be used by anyone to build linked data descriptions of images into graphs. She is also a research associate with the Florida Institute of Human and Machine Cognition, one of the primary organizers of a group called The Dataworthy Collective and is a member of the IPTC (International Press and Telecommunications Council) photo-metadata working group and the Research Data Alliance charter on Collections as Data.

As a researcher, Margaret's primary focus is at the intersection of semantics, metadata, knowledge representation and information science particularly around visual content, search and findability. She is deeply interested in how people describe what they experience visually and how to capture and formalize this knowledge into machine readable structures. She creates tools and processes for humans but augmented by machine intelligence. Many of these tools are useful for unifying the many types of metadata and descriptions of images - including the very important context element - into ontology infused knowledge graphs. Her tools can be used for tasks as advanced as complex domain modeling but can also facilitate image content to be shared and published while staying linked to it's metadata across workflows.

Learn more and connect with Margaret online

LinkedIn

Patreon

Bluesky

Substack

ImageSnippets

Metadata Authoring Systems

personal and art site

IPTC links

IPTC Photo Metadata

Software that supports IPTC Photo Metadata

Get IPTC Photo Metadata

Browser extensions for IPTC Photo Metadata

Resource not mentioned in podcast

(but very useful for examining structured metadata in web pages)

OpenLink Structured Data Sniffer (OSDS)

Video

Here’s the video version of our conversation:

https://youtu.be/pjoAAq5zuRk

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 21. Nowadays, we are all immersed in a deluge of information and media, especially images. The real value of these images is captured in the metadata about them. Without information about the history of an image, its technical details, and the context in which it originated, it might as well be a piece of random clip art. Margaret Warren is the creator of ImageSnippets, a tool that helps you create and manage the metadata that imbues your images with meaning.

Interview transcript

Larry:

Hi, everyone. Welcome to episode number 21 of the Knowledge Graph Insights podcast. I am really happy today to welcome to the show my friend, Margaret Warren. Margaret and I helped co-organize a weekly event called The Dataworthy Collective, which is a collaborative learning environment, but she's better known as the CEO and the CTO of a company called Metadata Authoring Systems, which is best known for the product, ImageSnippets, which does metadata management for photos and other kinds of images. Welcome, Margaret. Tell the folks a little bit more about what you're up to these days.

Margaret:

Hello! How are you doing? Yeah, it's a good introduction, thanks. Well, what I'd like to say about Metadata Authoring Systems is just that we want the most obscure images to be findable and easily accessible by describing and preserving them in the most precise ways possible. That's kind of what we try to do. One of the tools that we created is called ImageSnippets. It's been around for quite some time. ImageSnippets is, I think it can best be described as a way to take image description data itself and parse that into knowledge graphs, which is not something that people necessarily think of that much. It adds a whole new dimension to that content. You have an image, and what is the metadata that's attached to that image? How do you turn that metadata into a graph? Then, what can you do with that graph once you have it created?

Larry:

Yeah, no, that's super interesting too. A lot of people would back up from that. Like the use case, the end use case, it would be like, "Well, I want that picture of my daughter's 4th birthday party," or this specific product piece, or that famous Porsche engine manifold, the thing that you've example-

Margaret:

That I show that a lot.

Larry:

Exactly, yeah. Well, it sort of gets in, but I guess, I definitely want to focus on the use cases, but I think there's a few people, and even people who are versed in metadata from one perspective or another, may benefit from a bigger picture view of how you see the world of metadata. You come at it from image management in particular, but you're really a metadata strategist and management person at heart. Can you talk a little bit about what it is and why it's important?

Margaret:

Yeah. Well, I'll lead into this by talking a little bit about my background, because it helps to set the stage. I grew up with a photographer, my father was a photojournalist. In the 70s, he was working in a newspaper and I was growing up with this, and I was sort of the metadata person in the family without really understanding that exactly what it would lead to. I would go out and make photos. We were using Nikon film cameras then and everything, and we would go back to the dark room and develop the prints and everything. I would take these, I have a classic example that I used visually, but it's like I have the metadata written on the back of the photo. It's really cute. I was 10 years old, and it's like, Who's in the car? Where we were? Who took the picture? Who developed the picture? Who printed the picture? I even got into the administrative metadata. All this seemed important to me.

Margaret:

My mother used to write a little bit onto the slides and stuff a little bit, but somehow I just had this knack and I was very interested in this. Now, that continued on over the years because I've always also been an artist and a photographer and a technologist. I was in the Coast Guard and I was an electronics technician, and then I started working on computing systems and doing programming, and ultimately, had my own company doing hardware and networking support and building networks and deploying software solutions, lots of things. Then, I was trying to publish my images on the web and it's super interesting to me that the problem that I was trying to solve about publishing my images on the web almost 30 years ago is still, well, actually, I created a solution that solves it with ImageSnippets, but it's still something that is not really mainstream.

Margaret:

The entire vision that had to do with metadata and everything was sort of hijacked by the centralized companies that were just so data hungry and so ... well, engagement hungry rather like we want you to engage constantly, so we're going to make it increasingly easy for you to share your images as fast as you possibly can without thinking very carefully about the metadata aspect of what is in these images, why you're sharing it, why it's there. This is such a deep, deep, deep subject.

Margaret:

To answer your question about what is metadata, I often say that metadata really ultimately is data about anything that you're talking ... I can hold up this coffee cup and say, "Oh, I can describe the coffee cup. It's got flowers on it, the size of it, the shape of it, how warm it is, the temperature," anything that I really say about anything is metadata. But when it comes to, say, images that you're going to put on the web, and I like to narrow it down to images because that's really my forte, still images, but you can have, of course, metadata on PDF files and audio files and video files and everything can have metadata attached to it.

Margaret:

Inside of images, you can add headers and other types of information that would contain things like the EXIF data. A lot of people are very, very familiar with EXIF data, which is, that is the data that usually comes from hardware. That will come from a camera or it will come from a scanner or some piece of hardware that will say what the settings were. Sometimes it will give the GPS data, if you take an image with your smartphone, and it will include the GPS data unless you turn

Jans Aasman

Hybrid AI architectures get more complex every day. For Jans Aasman, large language models and generative AI are just the newest additions to his toolkit.

Jans has been building advanced hybrid AI systems for more than 15 years, using knowledge graphs, symbolic logic, and machine learning - and now LLMs and gen AI - to build advanced AI systems for Fortune 500 companies.

We talked about:

his knowledge graph and neuro-symbolic work as the CEO of Franz

the crucial role of a visionary knowledge graph champion in KG adoption in enterprises

the two types of KG champions he has encountered: the magic-seeking, forward-looking technologist and the more pragmatic IT leader trying to better organize their operation

the AI architectural patterns and themes he has seen emerge over the past 25 years: logic, reasoning, event-based KGs, machine learning, and of course gen AI and LLMs

how gen AI lets him do things he couldn't have imagined five years ago

the enduring importance of enterprise taxonomies, especially in RAG architectures which business entities need to be understood to answer complex business questions

his approach to neuro-symbolic AI, seeing it as a "fluid interplay between a knowledge graph, symbolic logic, machine learning, and generative AI"

the power of "magic predicates"

a common combination of AI technologies and human interactions that can improve medical diagnosis and care decisions

his strong belief in keeping humans in the loop in AI systems

his observation that technology and business leaders seeing the need for "a symbolic approach next to generative AI"

his take on the development of reasoning capabilities of LLMs

how the code-generation capabilities of LLMs are more beneficial to senior programmers and may even impede the work of less experiences coders

Jans' bio

Jans Aasman is a Ph.D. psychologist and expert in Cognitive Science - as well as CEO of Franz Inc., an early innovator in Artificial Intelligence and provider of Knowledge Graph Solutions based on AllegroGraph. As both a scientist and CEO, Dr. Aasman continues to break ground in the areas of Artificial Intelligence and Knowledge Graphs as he works hand-in-hand with numerous Fortune 500 organizations as well as government entities worldwide.

Connect with Jans online

LinkedIn

email: ja at franz dot com

Video

Here’s the video version of our conversation:

https://www.youtube.com/watch?v=SZBZxC8S1Uk

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 20. The mix of technologies in hybrid artificial intelligence systems just keeps getting more interesting. This might seem like a new phenomenon, but long before our LinkedIn feeds were clogged with posts about retrieval augmented generation and neuro-symbolic architectures, Jans Aasman was building AI systems that combined knowledge graphs, symbolic logic, and machine learning. Large language models and generative AI are just the newest technologies in his AI toolkit.

Interview transcript

Larry:

Hi, everyone. Welcome to episode number 20 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Jans Aasmann. Jans is, he originally started out as a psychologist and he got into cognitive science. For the past 20 years, he's run a company called Franz, where he's the CEO doing neuro-symbolic AI, so welcome, Jans. Tell the folks a little bit more about what you're doing these days.

Jans:

We help companies build knowledge graphs, but with the special angle that we now offer neuro-symbolic AI so that we, in a very fluid way, mix traditional symbolic logic and the traditional machine learning with the new generative AI. We do this in every possible combination that you could think of.

Larry:

Who?

Jans:

These applications might be in healthcare or in call centers or in publishing. It's many, many, many different domains it supplies.

Larry:

Is it mostly large enterprises or is there a certain scale at which this stuff works better?

Jans:

Our customers are always Fortune 500, Fortune 100 companies. It's all the companies that are trying to do innovation. Most big enterprises now believe that knowledge graphs is in their future. They're experimenting with it. They do experiments and we help them build their first knowledge graphs in most cases. Once they get that going, they can do it on their own.

Larry:

Interesting. It's often their first knowledge graph. Where in the organizations is this typically pioneered? As you come into an organization, is it IT or is it data science? Where are you typically entering the organization?

Jans:

That's actually the wrong question.

Larry:

Okay.

Jans:

The thing is, all the places where we're successful, there's a champion, a person that's really looking into the future and has this vision that it's possible to not build a new silo for every new problem, but there should be a way to integrate all the knowledge in the organization into something incredibly useful with that. You can't leave it to a single programmer or a single architect. It's usually someone with some business experience and also some architectural role that believes in this approach. If you leave it up to an IT department, it's just not another database, but it's not the philosophy of a knowledge graph of integrating knowledge. It's just, okay, this is a problem I can solve with the graph. Let's do it that way.

Jans:

You need a person that says, "Hey, I've got so many different sources of information in my organization. I know we're not combining it in the right way, and it's too complex to put in a relational database. I know we have to solve it with a thing that they now call knowledge graphs, but it's even more than that. I know partly it's data science. It's machine learning. Partly it's rule-based. It's the logic, symbolic logic. I also know that I need the new generative AI in this, but how do I do this all? This is incredibly complex. I can see the future, but how do I do it?" That's where we come in, help build them knowledge graphs, but with a symbolic angle.

Larry:

I love that, and that knowledge graphs are a key element in the architecture of the future and of the present, it sounds like. That champion who comes to you, it sounds like they're somebody who's been aware of the hazards and consequences of siloed information and data. Is that typically what they're coming in for, of how can we better integrate and understand all of this?

Jans:

Again, I would say there's two types of champions. One of them is just, they want magic. They see all the articles about generative AI. They see the Gartner articles, Forrester articles about knowledge graphs, and they think, "I have the feeling that something can be done with knowledge graphs and neuro-symbolic AI." Those people are not very super technical. They usually have a technical background but then went into business, but they know something can be done.

Jans:

Then you have the second type of champion, of people that are literally always over 35, that have spent their active life building application after application. Every time when they created a beautiful application where their bosses were really happy about, the sad thing is, they build a new silo and they made their whole enterprise even more complicated. These are the people that say, "You know what? There has to be a better way of integrating my knowledge." Those are the people that get interested in semantic technology and they say, "There has to be a way that we don't build new silos every time." It's this thing that we call data-centric computing. Data comes first and applications need to go on top, but we don't need to change the data all the time, rewrite and copy the data all the time. That's the disappointed IT person that says, "There has to be a better way." Does it make sense?

Larry:

Yes, that makes sense.

Jans:

One is really looking at the future like, "Wow, my company needs magic to make more revenues. The other one is, "Hey, we need to reorganize our IT house, because this is madness the way we do it now."

Larry:

Yeah. They don't want to do that, repeating the same mistake over and over again.

Jans:

No, let me try. How do I turn my sound off? Okay. Yeah.

Larry:

Cool. Yes. You said they're always older, because these folks have been around the block a few times who come in with that. I'm going to guess. Are they generally? It sounds like both. It sounds like maybe in the first type, the magic seekers, you're probably doing a lot of education, but with the second type, you're maybe just more finessing the implementation. Is that?

Jans:

No, it's a huge difference. The magic seeker will give you freedom to think together, "Okay, how are we going to do this? What is the first baby step we can do to show the rest of the company that this works?" You try to find the low-hanging fruit where you can show how neuro-symbolic AI and knowledge graphs can help and do things they couldn't do before, whereas the second type of champion, the disappointed IT person that says, "We have to find a new way," then it's way more IT-oriented. Let's find three databases where we want to do something extra with. Let's build a semantic data catalog. Let's build an ontology of the objects that we really care about in our business. Then let's see how we can replace an existing system by something that is 10 times more simple, 10 times more easy to understand, and 10 times easier to do data science with. Does that make sense? That second champion is very more IT-oriented and wants to make the flow within the company better, and the first one just wants more revenue by magic.

Larry:

Right.

Jans:

Huge difference. Huge difference in the two champions.

Larry:

Is it pretty much an even mix between those, or do you seek out one or the other more?

Jans:

No,...